Separate tables by subset

CLAUDE.md

@@ -10,16 +10,18 @@ fev-bench Leaderboard is a Streamlit web application displaying time series fore
 
 ```bash
 # Run the Streamlit app locally
-streamlit run fev-leaderboard-app.py --server.port=8501 --server.address=0.0.0.0
+uv run streamlit run fev-leaderboard-app.py --server.port=8501 --server.address=0.0.0.0
 
 # Regenerate leaderboard tables from autogluon/fev repo (defaults to main branch)
-python save_tables.py [commit]  # e.g., python save_tables.py abc123
+uv run python save_tables.py [commit]  # e.g., uv run python save_tables.py abc123
 
 # Docker build and run
 docker build -t fev-leaderboard .
 docker run -p 8501:8501 fev-leaderboard
 ```
 
+Note: Use `uv run` prefix for all Python commands in this project.
+
 No test or lint frameworks are configured.
 
 ## Architecture
@@ -33,8 +35,14 @@ pages/
 └── about.py               # Help page with links
 src/
 ├── utils.py               # Visualization, formatting, MODEL_CONFIG, color palette
-└── strings.py             # UI text, metric descriptions, paper citations
-tables/                    # Pre-generated CSVs (leaderboard_*, pairwise_*, pivot_*)
+├── strings.py             # UI text, metric descriptions, paper citations
+└── task_groups.py         # Task groupings by frequency and domain
+tables/                    # Pre-generated CSVs
+├── pivot_*.csv            # Full pivot tables (filtered in app by task group)
+├── summaries.csv          # Raw evaluation summaries
+└── {group}/               # Subdirectories for each task group (full, mini, frequency_*, domain_*)
+    ├── leaderboard_*.csv  # Leaderboard tables per metric
+    └── pairwise_*.csv     # Pairwise comparison tables per metric
 ```
 
 **Data flow**: GitHub (autogluon/fev) → `save_tables.py` → pre-computed tables → `fev_bench.py` visualization

pages/fev_bench.py

@@ -3,6 +3,7 @@ from pathlib import Path
 
 sys.path.append(str(Path(__file__).parent))
 
+import fev
 import pandas as pd
 import streamlit as st
 from streamlit.elements.lib.column_types import ColumnConfig
@@ -15,6 +16,13 @@ from src.strings import (
     PAIRWISE_BENCHMARK_DETAILS,
     get_pivot_legend,
 )
+from src.task_groups import (
+    ALL_TASKS,
+    DOMAIN_GROUPS,
+    FREQUENCY_GROUPS,
+    MINI_TASKS,
+    get_task_group,
+)
 from src.utils import (
     COLORS,
     construct_pairwise_chart,
@@ -29,15 +37,38 @@ TITLE = "<h1 style='text-align: center; font-size: 350%;'>fev-bench</h1>"
 SORT_COL = "win_rate"
 AVAILABLE_METRICS = ["SQL", "MASE", "WQL", "WAPE"]
 
+# Group type options
+GROUP_TYPES = ["Full benchmark", "fev-bench-mini", "By frequency", "By domain"]
+FREQUENCY_OPTIONS = list(FREQUENCY_GROUPS.keys())
+DOMAIN_OPTIONS = list(DOMAIN_GROUPS.keys())
+
+# Mapping from UI selections to table directory names
+GROUP_DIR_MAPPING = {
+    "Full benchmark": "full",
+    "fev-bench-mini": "mini",
+    "Sub-hourly": "frequency_sub_hourly",
+    "Hourly": "frequency_hourly",
+    "Daily": "frequency_daily",
+    "Weekly": "frequency_weekly",
+    "Monthly+": "frequency_monthly_plus",
+    "Energy": "domain_energy",
+    "Nature": "domain_nature",
+    "Cloud": "domain_cloud",
+    "Mobility": "domain_mobility",
+    "Econ": "domain_econ",
+    "Health": "domain_health",
+    "Retail": "domain_retail",
+}
+
 
 @st.cache_data()
-def get_leaderboard(metric_name: str) -> pd.DataFrame:
-    return pd.read_csv(f"tables/leaderboard_{metric_name}.csv")
+def get_leaderboard(metric_name: str, group_dir: str) -> pd.DataFrame:
+    return pd.read_csv(f"tables/{group_dir}/leaderboard_{metric_name}.csv")
 
 
 @st.cache_data()
-def get_pairwise(metric_name: str) -> pd.DataFrame:
-    return pd.read_csv(f"tables/pairwise_{metric_name}.csv")
+def get_pairwise(metric_name: str, group_dir: str) -> pd.DataFrame:
+    return pd.read_csv(f"tables/{group_dir}/pairwise_{metric_name}.csv")
 
 
 @st.cache_data()
@@ -52,13 +83,38 @@ with st.sidebar:
     selected_metric = st.selectbox("Evaluation Metric", options=AVAILABLE_METRICS, format_func=format_metric_name)
     st.caption(get_metric_description(selected_metric))
 
+    st.divider()
+
+    # Task group selection
+    selected_group_type = st.selectbox("Task Group", options=GROUP_TYPES)
+
+    # Conditional sub-selection for frequency/domain
+    selected_subgroup = None
+    if selected_group_type == "By frequency":
+        selected_subgroup = st.selectbox("Frequency", options=FREQUENCY_OPTIONS)
+    elif selected_group_type == "By domain":
+        selected_subgroup = st.selectbox("Domain", options=DOMAIN_OPTIONS)
+
+    # Determine the directory to load tables from
+    if selected_group_type in ["Full benchmark", "fev-bench-mini"]:
+        group_dir = GROUP_DIR_MAPPING[selected_group_type]
+        task_list = ALL_TASKS if selected_group_type == "Full benchmark" else MINI_TASKS
+    else:
+        group_dir = GROUP_DIR_MAPPING[selected_subgroup]
+        if selected_group_type == "By frequency":
+            task_list = FREQUENCY_GROUPS[selected_subgroup]
+        else:
+            task_list = DOMAIN_GROUPS[selected_subgroup]
+
+    st.caption(f"{len(task_list)} tasks")
+
 cols = st.columns(spec=[0.025, 0.95, 0.025])
 
 with cols[1] as main_container:
     st.markdown(TITLE, unsafe_allow_html=True)
 
-    metric_df = get_leaderboard(selected_metric).sort_values(by=SORT_COL, ascending=False)
-    pairwise_df = get_pairwise(selected_metric)
+    metric_df = get_leaderboard(selected_metric, group_dir).sort_values(by=SORT_COL, ascending=False)
+    pairwise_df = get_pairwise(selected_metric, group_dir)
 
     st.markdown("## :material/trophy: Leaderboard", unsafe_allow_html=True)
     st.markdown(FEV_BENCHMARK_BASIC_INFO, unsafe_allow_html=True)
@@ -71,7 +127,7 @@ with cols[1] as main_container:
             "model_name": ColumnConfig(label="Model Name", alignment="left"),
             "win_rate": st.column_config.NumberColumn(label="Avg. win rate (%)", format="%.1f"),
             "skill_score": st.column_config.NumberColumn(label="Skill score (%)", format="%.1f"),
-            "median_inference_time_s": st.column_config.NumberColumn(label="Median runtime (s)", format="%.1f"),
+            "median_inference_time_s_per100": st.column_config.NumberColumn(label="Median runtime (s / 100 series)", format="%.1f"),
             "training_corpus_overlap": st.column_config.NumberColumn(label="Leakage (%)", format="%d"),
             "num_failures": st.column_config.NumberColumn(label="Failed tasks (%)", format="%.0f"),
             "zero_shot": ColumnConfig(label="Zero-shot", alignment="center"),
@@ -109,6 +165,12 @@ with cols[1] as main_container:
         baseline_imputed = baseline_imputed.set_index("Task name")
         leakage_imputed = leakage_imputed.set_index("Task name")
 
+        # Filter pivot table to only show tasks in the selected group
+        available_tasks = [t for t in task_list if t in pivot_df.index]
+        pivot_df = pivot_df.loc[available_tasks]
+        baseline_imputed = baseline_imputed.loc[available_tasks]
+        leakage_imputed = leakage_imputed.loc[available_tasks]
+
         def style_pivot_table(errors, is_baseline_imputed, is_leakage_imputed):
             rank_colors = {1: COLORS["gold"], 2: COLORS["silver"], 3: COLORS["bronze"]}
 

save_tables.py

@@ -12,6 +12,7 @@ sys.path.append(str(Path(__file__).parent))
 import fev
 import pandas as pd
 
+from src.task_groups import ALL_TASKS, DOMAIN_GROUPS, FREQUENCY_GROUPS, MINI_TASKS
 from src.utils import format_leaderboard
 
 GITHUB_REPO = "autogluon/fev"
@@ -25,6 +26,24 @@ N_RESAMPLES_FOR_CI = 1000
 TOP_K_MODELS_TO_PLOT = 15
 AVAILABLE_METRICS = ["SQL", "MASE", "WQL", "WAPE"]
 
+# All task groups to generate tables for
+TASK_GROUPS = {
+    "full": ALL_TASKS,
+    "mini": MINI_TASKS,
+    "frequency_sub_hourly": FREQUENCY_GROUPS["Sub-hourly"],
+    "frequency_hourly": FREQUENCY_GROUPS["Hourly"],
+    "frequency_daily": FREQUENCY_GROUPS["Daily"],
+    "frequency_weekly": FREQUENCY_GROUPS["Weekly"],
+    "frequency_monthly_plus": FREQUENCY_GROUPS["Monthly+"],
+    "domain_energy": DOMAIN_GROUPS["Energy"],
+    "domain_nature": DOMAIN_GROUPS["Nature"],
+    "domain_cloud": DOMAIN_GROUPS["Cloud"],
+    "domain_mobility": DOMAIN_GROUPS["Mobility"],
+    "domain_econ": DOMAIN_GROUPS["Econ"],
+    "domain_health": DOMAIN_GROUPS["Health"],
+    "domain_retail": DOMAIN_GROUPS["Retail"],
+}
+
 
 def get_csv_files_from_github(commit: str) -> list[str]:
     """Get list of CSV file paths from the GitHub repo at a specific commit."""
@@ -65,6 +84,7 @@ def compute_leaderboard(summaries: pd.DataFrame, metric_name: str) -> pd.DataFra
         missing_strategy="impute",
         baseline_model=BASELINE_MODEL,
         leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
+        normalize_time_per_n_forecasts=100,
     )
     lb = lb.astype("float64").reset_index()
 
@@ -138,31 +158,54 @@ def main():
     print(f"Loading summaries from {GITHUB_REPO} at commit {args.commit}...")
     summaries = load_summaries_from_github(args.commit)
 
-    for metric in AVAILABLE_METRICS:
-        print(f"Processing {metric}...")
-
-        # Compute leaderboard
-        leaderboard_df = compute_leaderboard(summaries, metric)
-        leaderboard_df.to_csv(tables_dir / f"leaderboard_{metric}.csv", index=False)
-
-        # Get top models for pairwise comparison
-        top_k_models = (
-            leaderboard_df.sort_values(by=SORT_COL, ascending=False).head(TOP_K_MODELS_TO_PLOT)["model_name"].tolist()
-        )
-
-        # Compute pairwise comparison
-        pairwise_df = compute_pairwise(summaries, metric, top_k_models)
-        pairwise_df.to_csv(tables_dir / f"pairwise_{metric}.csv", index=False)
+    # Save raw summaries for on-the-fly subset computation
+    summaries.to_csv(tables_dir / "summaries.csv", index=False)
+    print("Saved: summaries.csv")
 
-        # Compute pivot table
+    # Generate pivot tables (full version only, at root level)
+    for metric in AVAILABLE_METRICS:
+        print(f"Processing pivot table for {metric}...")
         pivot_df, baseline_imputed, leakage_imputed = compute_pivot_table(summaries, metric)
         pivot_df.to_csv(tables_dir / f"pivot_{metric}.csv", index=False)
         baseline_imputed.to_csv(tables_dir / f"pivot_{metric}_baseline_imputed.csv", index=False)
         leakage_imputed.to_csv(tables_dir / f"pivot_{metric}_leakage_imputed.csv", index=False)
+        print(f"  Saved: pivot_{metric}.csv")
+
+    # Generate leaderboard and pairwise tables for each task group
+    for group_name, task_list in TASK_GROUPS.items():
+        print(f"\nProcessing group: {group_name} ({len(task_list)} tasks)...")
+
+        # Create subdirectory for this group
+        group_dir = tables_dir / group_name
+        group_dir.mkdir(exist_ok=True)
+
+        # Filter summaries to only include tasks in this group
+        group_summaries = summaries[summaries["task_name"].isin(task_list)]
+
+        if group_summaries.empty:
+            print(f"  WARNING: No matching tasks found for group {group_name}")
+            continue
+
+        actual_tasks = group_summaries["task_name"].nunique()
+        print(f"  Found {actual_tasks} tasks in summaries")
+
+        for metric in AVAILABLE_METRICS:
+            # Compute leaderboard for this group
+            leaderboard_df = compute_leaderboard(group_summaries, metric)
+            leaderboard_df.to_csv(group_dir / f"leaderboard_{metric}.csv", index=False)
+
+            # Get top models for pairwise comparison
+            top_k_models = (
+                leaderboard_df.sort_values(by=SORT_COL, ascending=False).head(TOP_K_MODELS_TO_PLOT)["model_name"].tolist()
+            )
+
+            # Compute pairwise comparison
+            pairwise_df = compute_pairwise(group_summaries, metric, top_k_models)
+            pairwise_df.to_csv(group_dir / f"pairwise_{metric}.csv", index=False)
 
-        print(f"  Saved: leaderboard_{metric}.csv, pairwise_{metric}.csv, pivot_{metric}.csv")
+            print(f"  Saved: {group_name}/leaderboard_{metric}.csv, {group_name}/pairwise_{metric}.csv")
 
-    print(f"All tables saved to {tables_dir}/")
+    print(f"\nAll tables saved to {tables_dir}/")
 
 
 if __name__ == "__main__":

src/task_groups.py

@@ -0,0 +1,178 @@
+"""Task groupings for filtering the leaderboard by subsets."""
+
+# All tasks in the benchmark
+ALL_TASKS = [
+    "ETT_15T", "ETT_1D", "ETT_1H", "ETT_1W",
+    "LOOP_SEATTLE_1D", "LOOP_SEATTLE_1H", "LOOP_SEATTLE_5T",
+    "M_DENSE_1D", "M_DENSE_1H",
+    "SZ_TAXI_15T", "SZ_TAXI_1H",
+    "australian_tourism",
+    "bizitobs_l2c_1H", "bizitobs_l2c_5T",
+    "boomlet_1062", "boomlet_1209", "boomlet_1225", "boomlet_1230", "boomlet_1282",
+    "boomlet_1487", "boomlet_1631", "boomlet_1676", "boomlet_1855", "boomlet_1975",
+    "boomlet_2187", "boomlet_285", "boomlet_619", "boomlet_772", "boomlet_963",
+    "ecdc_ili",
+    "entsoe_15T", "entsoe_1H", "entsoe_30T",
+    "epf_be", "epf_de", "epf_fr", "epf_np", "epf_pjm",
+    "ercot_1D", "ercot_1H", "ercot_1M", "ercot_1W",
+    "favorita_stores_1D", "favorita_stores_1M", "favorita_stores_1W",
+    "favorita_transactions_1D", "favorita_transactions_1M", "favorita_transactions_1W",
+    "fred_md_2025/cee", "fred_md_2025/macro",
+    "fred_qd_2025/cee", "fred_qd_2025/macro",
+    "gvar",
+    "hermes",
+    "hierarchical_sales_1D", "hierarchical_sales_1W",
+    "hospital", "hospital_admissions_1D", "hospital_admissions_1W",
+    "jena_weather_10T", "jena_weather_1D", "jena_weather_1H",
+    "kdd_cup_2022_10T", "kdd_cup_2022_1D", "kdd_cup_2022_30T",
+    "m5_1D", "m5_1M", "m5_1W",
+    "proenfo_gfc12", "proenfo_gfc14", "proenfo_gfc17",
+    "redset_15T", "redset_1H", "redset_5T",
+    "restaurant",
+    "rohlik_orders_1D", "rohlik_orders_1W", "rohlik_sales_1D", "rohlik_sales_1W",
+    "rossmann_1D", "rossmann_1W",
+    "solar_1D", "solar_1W", "solar_with_weather_15T", "solar_with_weather_1H",
+    "uci_air_quality_1D", "uci_air_quality_1H",
+    "uk_covid_nation_1D/cumulative", "uk_covid_nation_1D/new",
+    "uk_covid_nation_1W/cumulative", "uk_covid_nation_1W/new",
+    "uk_covid_utla_1D/new", "uk_covid_utla_1W/cumulative",
+    "us_consumption_1M", "us_consumption_1Q", "us_consumption_1Y",
+    "walmart",
+    "world_co2_emissions", "world_life_expectancy", "world_tourism",
+]
+
+# Mini benchmark - representative subset
+MINI_TASKS = [
+    "ETT_1H", "ETT_1D",
+    "LOOP_SEATTLE_1H",
+    "entsoe_1H",
+    "epf_de",
+    "ercot_1D",
+    "favorita_stores_1D",
+    "hospital",
+    "jena_weather_1H",
+    "m5_1D",
+    "rossmann_1D",
+    "solar_1D",
+    "walmart",
+    "australian_tourism",
+    "gvar",
+]
+
+# Frequency-based groupings
+FREQUENCY_GROUPS = {
+    "Sub-hourly": [
+        "ETT_15T", "LOOP_SEATTLE_5T", "SZ_TAXI_15T", "bizitobs_l2c_5T",
+        "entsoe_15T", "entsoe_30T", "jena_weather_10T", "kdd_cup_2022_10T",
+        "kdd_cup_2022_30T", "redset_15T", "redset_5T", "solar_with_weather_15T",
+    ],
+    "Hourly": [
+        "ETT_1H", "LOOP_SEATTLE_1H", "M_DENSE_1H", "SZ_TAXI_1H",
+        "bizitobs_l2c_1H", "entsoe_1H", "ercot_1H", "jena_weather_1H",
+        "redset_1H", "solar_with_weather_1H", "uci_air_quality_1H",
+    ],
+    "Daily": [
+        "ETT_1D", "LOOP_SEATTLE_1D", "M_DENSE_1D", "ercot_1D",
+        "favorita_stores_1D", "favorita_transactions_1D", "hierarchical_sales_1D",
+        "hospital_admissions_1D", "jena_weather_1D", "kdd_cup_2022_1D",
+        "m5_1D", "rohlik_orders_1D", "rohlik_sales_1D", "rossmann_1D",
+        "solar_1D", "uci_air_quality_1D", "uk_covid_nation_1D/cumulative",
+        "uk_covid_nation_1D/new", "uk_covid_utla_1D/new",
+    ],
+    "Weekly": [
+        "ETT_1W", "ercot_1W", "favorita_stores_1W", "favorita_transactions_1W",
+        "hierarchical_sales_1W", "hospital_admissions_1W", "m5_1W",
+        "rohlik_orders_1W", "rohlik_sales_1W", "rossmann_1W", "solar_1W",
+        "uk_covid_nation_1W/cumulative", "uk_covid_nation_1W/new",
+        "uk_covid_utla_1W/cumulative",
+    ],
+    "Monthly+": [
+        "ercot_1M", "favorita_stores_1M", "favorita_transactions_1M", "m5_1M",
+        "us_consumption_1M", "us_consumption_1Q", "us_consumption_1Y",
+        "fred_md_2025/cee", "fred_md_2025/macro", "fred_qd_2025/cee", "fred_qd_2025/macro",
+        "australian_tourism", "ecdc_ili", "gvar", "hospital", "restaurant",
+        "world_co2_emissions", "world_life_expectancy", "world_tourism",
+    ],
+}
+
+# Domain-based groupings
+DOMAIN_GROUPS = {
+    "Energy": [
+        "ETT_15T", "ETT_1D", "ETT_1H", "ETT_1W",
+        "entsoe_15T", "entsoe_1H", "entsoe_30T",
+        "epf_be", "epf_de", "epf_fr", "epf_np", "epf_pjm",
+        "ercot_1D", "ercot_1H", "ercot_1M", "ercot_1W",
+        "kdd_cup_2022_10T", "kdd_cup_2022_1D", "kdd_cup_2022_30T",
+        "proenfo_gfc12", "proenfo_gfc14", "proenfo_gfc17",
+        "solar_1D", "solar_1W", "solar_with_weather_15T", "solar_with_weather_1H",
+    ],
+    "Nature": [
+        "jena_weather_10T", "jena_weather_1D", "jena_weather_1H",
+        "uci_air_quality_1D", "uci_air_quality_1H",
+        "world_co2_emissions", "world_life_expectancy",
+    ],
+    "Cloud": [
+        "bizitobs_l2c_1H", "bizitobs_l2c_5T",
+        "redset_15T", "redset_1H", "redset_5T",
+        "M_DENSE_1D", "M_DENSE_1H",
+    ],
+    "Mobility": [
+        "LOOP_SEATTLE_1D", "LOOP_SEATTLE_1H", "LOOP_SEATTLE_5T",
+        "SZ_TAXI_15T", "SZ_TAXI_1H",
+        "world_tourism",
+    ],
+    "Econ": [
+        "fred_md_2025/cee", "fred_md_2025/macro",
+        "fred_qd_2025/cee", "fred_qd_2025/macro",
+        "us_consumption_1M", "us_consumption_1Q", "us_consumption_1Y",
+        "gvar",
+    ],
+    "Health": [
+        "hospital", "hospital_admissions_1D", "hospital_admissions_1W",
+        "ecdc_ili",
+        "uk_covid_nation_1D/cumulative", "uk_covid_nation_1D/new",
+        "uk_covid_nation_1W/cumulative", "uk_covid_nation_1W/new",
+        "uk_covid_utla_1D/new", "uk_covid_utla_1W/cumulative",
+    ],
+    "Retail": [
+        "australian_tourism",
+        "favorita_stores_1D", "favorita_stores_1M", "favorita_stores_1W",
+        "favorita_transactions_1D", "favorita_transactions_1M", "favorita_transactions_1W",
+        "hierarchical_sales_1D", "hierarchical_sales_1W",
+        "m5_1D", "m5_1M", "m5_1W",
+        "restaurant",
+        "rohlik_orders_1D", "rohlik_orders_1W", "rohlik_sales_1D", "rohlik_sales_1W",
+        "rossmann_1D", "rossmann_1W",
+        "walmart",
+        "hermes",
+        "boomlet_1062", "boomlet_1209", "boomlet_1225", "boomlet_1230", "boomlet_1282",
+        "boomlet_1487", "boomlet_1631", "boomlet_1676", "boomlet_1855", "boomlet_1975",
+        "boomlet_2187", "boomlet_285", "boomlet_619", "boomlet_772", "boomlet_963",
+    ],
+}
+
+
+def get_task_group(group_type: str, group_value: str | None = None) -> list[str]:
+    """Get the list of tasks for a given group type and value.
+
+    Args:
+        group_type: One of "full", "mini", "frequency", "domain"
+        group_value: Required for "frequency" and "domain" types
+
+    Returns:
+        List of task names belonging to the group
+    """
+    if group_type == "full":
+        return ALL_TASKS
+    elif group_type == "mini":
+        return MINI_TASKS
+    elif group_type == "frequency":
+        if group_value is None:
+            raise ValueError("group_value required for frequency grouping")
+        return FREQUENCY_GROUPS[group_value]
+    elif group_type == "domain":
+        if group_value is None:
+            raise ValueError("group_value required for domain grouping")
+        return DOMAIN_GROUPS[group_value]
+    else:
+        raise ValueError(f"Unknown group_type: {group_type}")

src/utils.py

@@ -151,7 +151,7 @@ def format_leaderboard(df: pd.DataFrame):
             "model_name",
             "win_rate",
             "skill_score",
-            "median_inference_time_s",
+            "median_inference_time_s_per100",
             "training_corpus_overlap",
             "num_failures",
             "zero_shot",

tables/domain_cloud/leaderboard_MASE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,91.83673469387756,38.05245966764596,0.0,1.1918681776984126,0.0,0.0
+TimesFM-2.5,90.81632653061226,36.140871018314456,0.0,5.796362790254237,0.0,0.0
+TiRex,72.44897959183675,28.401200313055885,0.0,0.5035950857142858,0.0,0.0
+Toto-1.0,72.44897959183672,29.457840087014375,0.0,51.15623996325397,0.0,0.0
+TabPFN-TS,67.3469387755102,23.42201367210007,0.0,187.34055617133333,0.0,0.0
+Sundial-Base,65.3061224489796,28.041386853187323,0.0,7.982507160666666,0.0,0.0
+Moirai-2.0,63.26530612244898,27.355195223904172,0.0,1.205957883968254,0.5714285714285714,0.0
+Chronos-Bolt,48.9795918367347,15.519602291715417,0.0,0.5222312566666667,0.0,0.0
+Stat. Ensemble,40.306122448979586,1.8933676693514423,0.0,537.2896767779606,0.0,14.285714285714285
+AutoARIMA,33.163265306122454,-1.7785743452331637,0.0,413.94069237515214,0.0,14.285714285714285
+AutoETS,29.59183673469387,3.9948381437565517,0.0,7.19858586,0.0,28.57142857142857
+Seasonal Naive,28.57142857142857,0.0,0.0,0.6601822176666666,0.0,0.0
+AutoTheta,22.448979591836736,-12.949758939559475,0.0,4.9961080071428565,0.0,0.0
+Naive,19.387755102040813,-155.35963356905293,0.0,0.7404548423333334,0.0,0.0
+Drift,4.081632653061225,-174.3872081501419,0.0,0.7429390493333333,0.0,0.0

tables/domain_cloud/leaderboard_SQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,93.87755102040816,49.490930346405136,0.0,1.1918681776984126,0.0,0.0
+TimesFM-2.5,88.77551020408163,47.50014141652608,0.0,5.796362790254237,0.0,0.0
+TiRex,75.51020408163265,41.22969676780125,0.0,0.5035950857142858,0.0,0.0
+Toto-1.0,73.46938775510203,41.85090900496293,0.0,51.15623996325397,0.0,0.0
+TabPFN-TS,70.40816326530611,40.949152552893196,0.0,187.34055617133333,0.0,0.0
+Moirai-2.0,64.28571428571426,38.16384284705047,0.0,1.205957883968254,0.5714285714285714,0.0
+Sundial-Base,62.244897959183675,38.72536792163377,0.0,7.982507160666666,0.0,0.0
+Chronos-Bolt,51.020408163265316,29.529054869503735,0.0,0.5222312566666667,0.0,0.0
+Stat. Ensemble,40.30612244897959,3.5776890137698247,0.0,537.2896767779606,0.0,14.285714285714285
+AutoARIMA,35.204081632653065,7.303665966467543,0.0,413.94069237515214,0.0,14.285714285714285
+AutoETS,26.530612244897956,-62.75686000684577,0.0,7.19858586,0.0,28.57142857142857
+Seasonal Naive,23.46938775510204,0.0,0.0,0.6601822176666666,0.0,0.0
+AutoTheta,19.387755102040813,-81.00666949062581,0.0,4.9961080071428565,0.0,0.0
+Naive,19.387755102040813,-229.40102452200532,0.0,0.7404548423333334,0.0,0.0
+Drift,6.122448979591836,-246.64002849728638,0.0,0.7429390493333333,0.0,0.0

tables/domain_cloud/leaderboard_WAPE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,98.9795918367347,56.03346299319774,0.0,1.1918681776984126,0.0,0.0
+TimesFM-2.5,81.63265306122449,48.21786745822298,0.0,5.796362790254237,0.0,0.0
+TabPFN-TS,81.63265306122447,48.7000590002878,0.0,187.34055617133333,0.0,0.0
+Moirai-2.0,71.42857142857142,43.82087074087539,0.0,1.205957883968254,0.5714285714285714,0.0
+Toto-1.0,68.36734693877553,41.61099035591892,0.0,51.15623996325397,0.0,0.0
+TiRex,66.32653061224491,43.211573262995806,0.0,0.5035950857142858,0.0,0.0
+Chronos-Bolt,63.26530612244898,41.3524784258674,0.0,0.5222312566666667,0.0,0.0
+Sundial-Base,63.26530612244898,44.73884694559522,0.0,7.982507160666666,0.0,0.0
+Stat. Ensemble,34.183673469387756,14.464019735134869,0.0,537.2896767779606,0.0,14.285714285714285
+AutoETS,28.57142857142857,-14.23026831192631,0.0,7.19858586,0.0,28.57142857142857
+AutoARIMA,26.02040816326531,5.087762742948254,0.0,413.94069237515214,0.0,14.285714285714285
+Naive,21.428571428571423,-20.643758684236204,0.0,0.7404548423333334,0.0,0.0
+Seasonal Naive,20.408163265306122,0.0,0.0,0.6601822176666666,0.0,0.0
+AutoTheta,18.36734693877551,3.978899086144194,0.0,4.9961080071428565,0.0,0.0
+Drift,6.1224489795918355,-31.5302295862653,0.0,0.7429390493333333,0.0,0.0

tables/domain_cloud/leaderboard_WQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,98.9795918367347,65.98215550494277,0.0,1.1918681776984126,0.0,0.0
+TimesFM-2.5,83.67346938775509,59.57802369715834,0.0,5.796362790254237,0.0,0.0
+TabPFN-TS,82.6530612244898,60.40100082559561,0.0,187.34055617133333,0.0,0.0
+TiRex,75.51020408163266,55.598937937465834,0.0,0.5035950857142858,0.0,0.0
+Moirai-2.0,70.40816326530611,55.32886174057864,0.0,1.205957883968254,0.5714285714285714,0.0
+Toto-1.0,66.32653061224491,54.42827487532307,0.0,51.15623996325397,0.0,0.0
+Chronos-Bolt,63.26530612244898,53.22642686457293,0.0,0.5222312566666667,0.0,0.0
+Sundial-Base,59.18367346938776,55.40077093761375,0.0,7.982507160666666,0.0,0.0
+Stat. Ensemble,34.183673469387756,10.25845553392335,0.0,537.2896767779606,0.0,14.285714285714285
+AutoARIMA,30.102040816326525,12.554876196379128,0.0,413.94069237515214,0.0,14.285714285714285
+Seasonal Naive,21.428571428571427,0.0,0.0,0.6601822176666666,0.0,0.0
+AutoTheta,19.387755102040813,-33.30969552158369,0.0,4.9961080071428565,0.0,0.0
+AutoETS,18.36734693877551,-109.16593122552642,0.0,7.19858586,0.0,28.57142857142857
+Naive,18.36734693877551,-89.49216169864154,0.0,0.7404548423333334,0.0,0.0
+Drift,8.16326530612245,-99.7014127864285,0.0,0.7429390493333333,0.0,0.0

tables/domain_cloud/pairwise_MASE.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
+Chronos-2,TimesFM-2.5,0.571,0.143,0.857,0.03,-0.022,0.079
+Chronos-2,TiRex,0.714,0.429,1.0,0.135,0.046,0.229
+Chronos-2,Toto-1.0,0.857,0.571,1.0,0.122,0.038,0.203
+Chronos-2,TabPFN-TS,0.857,0.571,1.0,0.191,0.061,0.341
+Chronos-2,Sundial-Base,0.857,0.571,1.0,0.139,0.048,0.214
+Chronos-2,Moirai-2.0,1.0,1.0,1.0,0.147,0.067,0.233
+Chronos-2,Chronos-Bolt,1.0,1.0,1.0,0.267,0.15,0.363
+Chronos-2,Stat. Ensemble,1.0,1.0,1.0,0.369,0.233,0.499
+Chronos-2,AutoARIMA,1.0,1.0,1.0,0.391,0.271,0.505
+Chronos-2,AutoETS,1.0,1.0,1.0,0.355,0.25,0.46
+Chronos-2,Seasonal Naive,1.0,1.0,1.0,0.381,0.238,0.51
+Chronos-2,AutoTheta,1.0,1.0,1.0,0.452,0.35,0.551
+Chronos-2,Naive,1.0,1.0,1.0,0.757,0.551,0.886
+Chronos-2,Drift,1.0,1.0,1.0,0.774,0.588,0.89
+TimesFM-2.5,Chronos-2,0.429,0.143,0.857,-0.031,-0.085,0.022
+TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
+TimesFM-2.5,TiRex,0.857,0.571,1.0,0.108,0.049,0.176
+TimesFM-2.5,Toto-1.0,0.857,0.571,1.0,0.095,0.034,0.149
+TimesFM-2.5,TabPFN-TS,0.714,0.429,1.0,0.166,0.024,0.347
+TimesFM-2.5,Sundial-Base,0.857,0.571,1.0,0.113,-0.009,0.213
+TimesFM-2.5,Moirai-2.0,1.0,1.0,1.0,0.121,0.059,0.189
+TimesFM-2.5,Chronos-Bolt,1.0,1.0,1.0,0.244,0.122,0.35
+TimesFM-2.5,Stat. Ensemble,1.0,1.0,1.0,0.349,0.227,0.474
+TimesFM-2.5,AutoARIMA,1.0,1.0,1.0,0.373,0.266,0.479
+TimesFM-2.5,AutoETS,1.0,1.0,1.0,0.335,0.224,0.453
+TimesFM-2.5,Seasonal Naive,1.0,1.0,1.0,0.361,0.224,0.486
+TimesFM-2.5,AutoTheta,1.0,1.0,1.0,0.435,0.324,0.53
+TimesFM-2.5,Naive,1.0,1.0,1.0,0.75,0.518,0.887
+TimesFM-2.5,Drift,1.0,1.0,1.0,0.767,0.558,0.891
+TiRex,Chronos-2,0.286,0.0,0.571,-0.156,-0.297,-0.049
+TiRex,TimesFM-2.5,0.143,0.0,0.429,-0.121,-0.213,-0.052
+TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
+TiRex,Toto-1.0,0.286,0.0,0.571,-0.015,-0.072,0.045
+TiRex,TabPFN-TS,0.429,0.143,0.714,0.065,-0.129,0.262
+TiRex,Sundial-Base,0.571,0.143,0.857,0.005,-0.2,0.132
+TiRex,Moirai-2.0,0.714,0.286,1.0,0.014,-0.031,0.05
+TiRex,Chronos-Bolt,0.857,0.571,1.0,0.152,-0.001,0.287
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+TiRex,AutoETS,1.0,1.0,1.0,0.254,0.108,0.403
+TiRex,Seasonal Naive,1.0,1.0,1.0,0.284,0.155,0.42
+TiRex,AutoTheta,1.0,1.0,1.0,0.366,0.233,0.475
+TiRex,Naive,0.857,0.571,1.0,0.72,0.431,0.876
+TiRex,Drift,1.0,1.0,1.0,0.739,0.489,0.882
+Toto-1.0,Chronos-2,0.143,0.0,0.429,-0.139,-0.254,-0.04
+Toto-1.0,TimesFM-2.5,0.143,0.0,0.429,-0.105,-0.176,-0.035
+Toto-1.0,TiRex,0.714,0.429,1.0,0.015,-0.047,0.067
+Toto-1.0,Toto-1.0,0.5,0.5,0.5,0.0,0.0,0.0
+Toto-1.0,TabPFN-TS,0.429,0.143,0.857,0.079,-0.086,0.285
+Toto-1.0,Sundial-Base,0.571,0.143,0.857,0.02,-0.16,0.156
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+Toto-1.0,Chronos-Bolt,0.571,0.143,0.857,0.165,-0.02,0.309
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+Toto-1.0,AutoARIMA,1.0,1.0,1.0,0.307,0.186,0.439
+Toto-1.0,AutoETS,1.0,1.0,1.0,0.265,0.136,0.391
+Toto-1.0,Seasonal Naive,1.0,1.0,1.0,0.295,0.179,0.422
+Toto-1.0,AutoTheta,1.0,1.0,1.0,0.375,0.221,0.491
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+TabPFN-TS,Chronos-2,0.143,0.0,0.429,-0.236,-0.517,-0.065
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+TabPFN-TS,TabPFN-TS,0.5,0.5,0.5,0.0,0.0,0.0
+TabPFN-TS,Sundial-Base,0.429,0.143,0.857,-0.064,-0.24,0.078
+TabPFN-TS,Moirai-2.0,0.571,0.143,0.857,-0.054,-0.294,0.11
+TabPFN-TS,Chronos-Bolt,0.571,0.143,0.857,0.094,-0.113,0.281
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+TabPFN-TS,AutoETS,0.857,0.571,1.0,0.202,-0.092,0.392
+TabPFN-TS,Seasonal Naive,0.857,0.571,1.0,0.234,-0.09,0.436
+TabPFN-TS,AutoTheta,0.857,0.571,1.0,0.322,0.125,0.49
+TabPFN-TS,Naive,1.0,1.0,1.0,0.7,0.48,0.83
+TabPFN-TS,Drift,1.0,1.0,1.0,0.721,0.526,0.84
+Sundial-Base,Chronos-2,0.143,0.0,0.429,-0.162,-0.273,-0.05
+Sundial-Base,TimesFM-2.5,0.143,0.0,0.429,-0.127,-0.27,0.009
+Sundial-Base,TiRex,0.429,0.143,0.857,-0.005,-0.152,0.166
+Sundial-Base,Toto-1.0,0.429,0.143,0.857,-0.02,-0.184,0.138
+Sundial-Base,TabPFN-TS,0.571,0.143,0.857,0.06,-0.084,0.194
+Sundial-Base,Sundial-Base,0.5,0.5,0.5,0.0,0.0,0.0
+Sundial-Base,Moirai-2.0,0.286,0.0,0.714,0.009,-0.115,0.172
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+Sundial-Base,Seasonal Naive,0.857,0.571,1.0,0.28,0.066,0.452
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+Moirai-2.0,Moirai-2.0,0.5,0.5,0.5,0.0,0.0,0.0
+Moirai-2.0,Chronos-Bolt,0.714,0.5,0.857,0.14,0.0,0.266
+Moirai-2.0,Stat. Ensemble,0.857,0.571,1.0,0.26,0.086,0.418
+Moirai-2.0,AutoARIMA,0.857,0.571,1.0,0.286,0.143,0.422
+Moirai-2.0,AutoETS,0.857,0.571,1.0,0.243,0.077,0.408
+Moirai-2.0,Seasonal Naive,0.857,0.571,1.0,0.274,0.108,0.436
+Moirai-2.0,AutoTheta,1.0,1.0,1.0,0.357,0.209,0.476
+Moirai-2.0,Naive,0.857,0.571,1.0,0.716,0.444,0.869
+Moirai-2.0,Drift,1.0,1.0,1.0,0.735,0.49,0.876
+Chronos-Bolt,Chronos-2,0.0,0.0,0.0,-0.364,-0.571,-0.176
+Chronos-Bolt,TimesFM-2.5,0.0,0.0,0.0,-0.323,-0.538,-0.138
+Chronos-Bolt,TiRex,0.143,0.0,0.429,-0.18,-0.403,0.001
+Chronos-Bolt,Toto-1.0,0.429,0.143,0.857,-0.198,-0.448,0.02
+Chronos-Bolt,TabPFN-TS,0.429,0.143,0.857,-0.103,-0.392,0.101
+Chronos-Bolt,Sundial-Base,0.286,0.0,0.714,-0.174,-0.407,0.01
+Chronos-Bolt,Moirai-2.0,0.286,0.143,0.5,-0.163,-0.362,0.0
+Chronos-Bolt,Chronos-Bolt,0.5,0.5,0.5,0.0,0.0,0.0
+Chronos-Bolt,Stat. Ensemble,0.714,0.429,1.0,0.139,-0.14,0.36
+Chronos-Bolt,AutoARIMA,0.714,0.429,1.0,0.17,-0.089,0.376
+Chronos-Bolt,AutoETS,0.571,0.286,1.0,0.12,-0.149,0.358
+Chronos-Bolt,Seasonal Naive,0.571,0.286,1.0,0.155,-0.161,0.412
+Chronos-Bolt,AutoTheta,0.857,0.571,1.0,0.252,0.033,0.435
+Chronos-Bolt,Naive,0.857,0.571,1.0,0.669,0.403,0.834
+Chronos-Bolt,Drift,1.0,1.0,1.0,0.692,0.463,0.841
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+Stat. Ensemble,TiRex,0.0,0.0,0.0,-0.37,-0.683,-0.137
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tables/domain_cloud/pairwise_SQL.csv

@@ -0,0 +1,226 @@
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tables/domain_cloud/pairwise_WAPE.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
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tables/domain_cloud/pairwise_WQL.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
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+Sundial-Base,Seasonal Naive,1.0,1.0,1.0,0.554,0.401,0.692
+Sundial-Base,AutoTheta,1.0,1.0,1.0,0.665,0.562,0.745
+Sundial-Base,AutoETS,1.0,1.0,1.0,0.787,0.484,0.931
+Sundial-Base,Naive,1.0,1.0,1.0,0.765,0.679,0.824
+Sundial-Base,Drift,1.0,1.0,1.0,0.777,0.699,0.835
+Stat. Ensemble,Chronos-2,0.0,0.0,0.0,-1.638,-2.549,-1.019
+Stat. Ensemble,TimesFM-2.5,0.0,0.0,0.0,-1.22,-1.982,-0.767
+Stat. Ensemble,TabPFN-TS,0.0,0.0,0.0,-1.266,-2.003,-0.812
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+Stat. Ensemble,Stat. Ensemble,0.5,0.5,0.5,0.0,0.0,0.0
+Stat. Ensemble,AutoARIMA,0.357,0.071,0.714,-0.026,-0.137,0.079
+Stat. Ensemble,Seasonal Naive,0.786,0.5,1.0,0.103,-0.137,0.273
+Stat. Ensemble,AutoTheta,1.0,1.0,1.0,0.327,0.129,0.506
+Stat. Ensemble,AutoETS,0.786,0.5,1.0,0.571,-0.123,0.874
+Stat. Ensemble,Naive,0.857,0.571,1.0,0.526,0.309,0.661
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+AutoARIMA,Chronos-2,0.0,0.0,0.0,-1.571,-2.67,-0.884
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+AutoARIMA,TiRex,0.0,0.0,0.0,-0.969,-1.687,-0.553
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+AutoARIMA,Chronos-Bolt,0.0,0.0,0.0,-0.87,-1.59,-0.497
+AutoARIMA,Sundial-Base,0.0,0.0,0.0,-0.961,-1.586,-0.531
+AutoARIMA,Stat. Ensemble,0.643,0.286,0.929,0.026,-0.086,0.12
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+AutoARIMA,AutoETS,0.643,0.286,0.929,0.582,-0.076,0.877
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+Seasonal Naive,Chronos-2,0.0,0.0,0.0,-1.94,-3.623,-1.073
+Seasonal Naive,TimesFM-2.5,0.0,0.0,0.0,-1.474,-2.58,-0.915
+Seasonal Naive,TabPFN-TS,0.0,0.0,0.0,-1.525,-2.706,-0.924
+Seasonal Naive,TiRex,0.0,0.0,0.0,-1.252,-2.289,-0.773
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+Seasonal Naive,Toto-1.0,0.0,0.0,0.0,-1.194,-2.015,-0.792
+Seasonal Naive,Chronos-Bolt,0.0,0.0,0.0,-1.138,-2.239,-0.653
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+Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0
+Seasonal Naive,AutoTheta,0.714,0.429,1.0,0.25,-0.111,0.492
+Seasonal Naive,AutoETS,0.429,0.143,0.714,0.522,-0.233,0.857
+Seasonal Naive,Naive,0.714,0.429,1.0,0.472,0.081,0.676
+Seasonal Naive,Drift,0.714,0.429,1.0,0.499,0.138,0.694
+AutoTheta,Chronos-2,0.0,0.0,0.0,-2.919,-4.075,-1.959
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+AutoTheta,AutoARIMA,0.286,0.0,0.571,-0.524,-1.15,-0.128
+AutoTheta,Seasonal Naive,0.286,0.0,0.571,-0.333,-0.969,0.1
+AutoTheta,AutoTheta,0.5,0.5,0.5,0.0,0.0,0.0
+AutoTheta,AutoETS,0.571,0.286,0.857,0.363,-0.461,0.761
+AutoTheta,Naive,0.714,0.429,1.0,0.296,0.088,0.445
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+AutoETS,AutoETS,0.5,0.5,0.5,0.0,0.0,0.0
+AutoETS,Naive,0.429,0.143,0.714,-0.104,-2.134,0.59
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+Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/domain_econ/leaderboard_MASE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Stat. Ensemble,90.17857142857143,45.72271417010185,0.0,122.9322408277356,0.0,0.0
+TiRex,77.67857142857143,44.68528567939526,0.0,0.18221935274193551,0.0,0.0
+AutoETS,75.0,43.8562914987522,0.0,5.3983249310847565,0.0,0.0
+Toto-1.0,72.32142857142857,43.03907891600834,0.0,7.622120549525617,0.25,0.0
+Chronos-2,71.42857142857143,43.58823324343927,0.0,0.16078357790322584,0.0,0.0
+Drift,54.46428571428571,39.92981801659404,0.0,0.5798891508064516,0.0,0.0
+AutoARIMA,52.67857142857143,40.48569891732045,0.0,22.892829618333334,0.0,0.0
+AutoTheta,51.78571428571429,39.29970062283895,0.0,1.519237516948134,0.0,0.0
+TimesFM-2.5,50.89285714285714,40.2381308594362,0.0,0.3558541395777989,0.25,0.0
+Moirai-2.0,50.0,38.98358451041862,0.0,0.23928379935483873,0.25,0.0
+Chronos-Bolt,45.535714285714285,39.07806454283599,0.0,0.20812267177419355,0.0,0.0
+TabPFN-TS,26.785714285714285,31.96593439462625,0.0,31.315901102580646,0.0,0.0
+Sundial-Base,16.964285714285715,27.148269806722414,0.0,8.033898541935486,0.0,0.0
+Naive,13.839285714285715,25.907009582259498,0.0,0.6308955793548388,0.0,0.0
+Seasonal Naive,0.4464285714285714,0.0,0.0,0.6923838372580645,0.0,0.0

tables/domain_econ/leaderboard_SQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+TiRex,90.17857142857143,45.89296124228933,0.0,0.18221935274193551,0.0,0.0
+Chronos-2,85.71428571428571,44.83531465181275,0.0,0.16078357790322584,0.0,0.0
+Toto-1.0,78.57142857142857,43.917307786456576,0.0,7.622120549525617,0.25,0.0
+Stat. Ensemble,77.67857142857143,44.003964088540535,0.0,122.9322408277356,0.0,0.0
+AutoETS,68.75,42.51990893939105,0.0,5.3983249310847565,0.0,0.0
+TimesFM-2.5,58.03571428571429,40.589714436687586,0.0,0.3558541395777989,0.25,0.0
+Moirai-2.0,54.46428571428571,39.72839855687115,0.0,0.23928379935483873,0.25,0.0
+Chronos-Bolt,53.57142857142857,40.67928797448308,0.0,0.20812267177419355,0.0,0.0
+AutoARIMA,44.642857142857146,37.817317026771654,0.0,22.892829618333334,0.0,0.0
+Drift,41.07142857142857,36.80178041975791,0.0,0.5798891508064516,0.0,0.0
+AutoTheta,36.607142857142854,35.9096442302903,0.0,1.519237516948134,0.0,0.0
+TabPFN-TS,33.92857142857143,35.64829103376008,0.0,31.315901102580646,0.0,0.0
+Sundial-Base,13.392857142857142,22.9740045287535,0.0,8.033898541935486,0.0,0.0
+Naive,12.946428571428573,22.54165568761486,0.0,0.6308955793548388,0.0,0.0
+Seasonal Naive,0.4464285714285714,0.0,0.0,0.6923838372580645,0.0,0.0

tables/domain_econ/leaderboard_WAPE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+TiRex,83.92857142857143,46.98885754022878,0.0,0.18221935274193551,0.0,0.0
+Toto-1.0,77.67857142857143,45.23136019790227,0.0,7.622120549525617,0.25,0.0
+Chronos-2,72.32142857142857,45.44326979757438,0.0,0.16078357790322584,0.0,0.0
+Stat. Ensemble,67.85714285714286,43.912437498598024,0.0,122.9322408277356,0.0,0.0
+TimesFM-2.5,66.96428571428571,44.549103435681026,0.0,0.3558541395777989,0.25,0.0
+Moirai-2.0,60.71428571428571,40.41824623531372,0.0,0.23928379935483873,0.25,0.0
+Chronos-Bolt,58.92857142857143,41.531861389854654,0.0,0.20812267177419355,0.0,0.0
+AutoETS,55.35714285714286,42.0098616544033,0.0,5.3983249310847565,0.0,0.0
+TabPFN-TS,46.42857142857143,37.52064439642875,0.0,31.315901102580646,0.0,0.0
+Drift,45.535714285714285,35.93207741626288,0.0,0.5798891508064516,0.0,0.0
+AutoARIMA,43.75,42.36950034572045,0.0,22.892829618333334,0.0,0.0
+AutoTheta,35.714285714285715,34.9733628053867,0.0,1.519237516948134,0.0,0.0
+Naive,21.875,27.215123726980593,0.0,0.6308955793548388,0.0,0.0
+Sundial-Base,12.5,26.36278133837574,0.0,8.033898541935486,0.0,0.0
+Seasonal Naive,0.4464285714285714,0.0,0.0,0.6923838372580645,0.0,0.0

tables/domain_econ/leaderboard_WQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+TiRex,88.39285714285714,51.91477926789758,0.0,0.18221935274193551,0.0,0.0
+Toto-1.0,83.92857142857143,50.15772831185203,0.0,7.622120549525617,0.25,0.0
+Chronos-2,83.03571428571429,50.35490090839225,0.0,0.16078357790322584,0.0,0.0
+TimesFM-2.5,75.0,49.42198208023613,0.0,0.3558541395777989,0.25,0.0
+Moirai-2.0,70.53571428571429,45.62669510318664,0.0,0.23928379935483873,0.25,0.0
+Chronos-Bolt,66.07142857142857,47.23739479366008,0.0,0.20812267177419355,0.0,0.0
+AutoETS,55.35714285714286,45.99017074595535,0.0,5.3983249310847565,0.0,0.0
+TabPFN-TS,52.67857142857143,43.99921543221276,0.0,31.315901102580646,0.0,0.0
+Stat. Ensemble,51.78571428571429,44.05004183245015,0.0,122.9322408277356,0.0,0.0
+AutoARIMA,36.607142857142854,40.963713207000154,0.0,22.892829618333334,0.0,0.0
+Drift,29.464285714285715,33.203638309970195,0.0,0.5798891508064516,0.0,0.0
+AutoTheta,25.0,32.478533527609585,0.0,1.519237516948134,0.0,0.0
+Sundial-Base,18.75,29.140422145178178,0.0,8.033898541935486,0.0,0.0
+Naive,12.946428571428573,24.05130462838899,0.0,0.6308955793548388,0.0,0.0
+Seasonal Naive,0.4464285714285714,0.0,0.0,0.6923838372580645,0.0,0.0

tables/domain_econ/pairwise_MASE.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+Stat. Ensemble,Stat. Ensemble,0.5,0.5,0.5,0.0,0.0,0.0
+Stat. Ensemble,TiRex,0.75,0.375,1.0,0.019,-0.012,0.054
+Stat. Ensemble,AutoETS,0.75,0.5,1.0,0.033,0.009,0.064
+Stat. Ensemble,Toto-1.0,0.625,0.25,0.878,0.047,-0.014,0.12
+Stat. Ensemble,Chronos-2,0.875,0.625,1.0,0.038,-0.002,0.092
+Stat. Ensemble,Drift,1.0,1.0,1.0,0.096,0.058,0.137
+Stat. Ensemble,AutoARIMA,0.875,0.625,1.0,0.088,0.022,0.181
+Stat. Ensemble,AutoTheta,1.0,1.0,1.0,0.106,0.063,0.149
+Stat. Ensemble,TimesFM-2.5,1.0,1.0,1.0,0.092,0.048,0.146
+Stat. Ensemble,Moirai-2.0,0.875,0.625,1.0,0.11,0.041,0.184
+Stat. Ensemble,Chronos-Bolt,0.875,0.625,1.0,0.109,0.047,0.179
+Stat. Ensemble,TabPFN-TS,1.0,1.0,1.0,0.202,0.141,0.276
+Stat. Ensemble,Sundial-Base,1.0,1.0,1.0,0.255,0.179,0.34
+Stat. Ensemble,Naive,1.0,1.0,1.0,0.267,0.164,0.373
+Stat. Ensemble,Seasonal Naive,1.0,1.0,1.0,0.457,0.368,0.548
+TiRex,Stat. Ensemble,0.25,0.0,0.625,-0.019,-0.057,0.012
+TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
+TiRex,AutoETS,0.5,0.125,0.875,0.015,-0.005,0.035
+TiRex,Toto-1.0,0.5,0.125,0.875,0.029,-0.058,0.124
+TiRex,Chronos-2,0.625,0.25,1.0,0.019,-0.013,0.052
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+TiRex,AutoARIMA,0.625,0.25,0.878,0.071,-0.023,0.187
+TiRex,AutoTheta,0.875,0.625,1.0,0.089,0.044,0.131
+TiRex,TimesFM-2.5,0.875,0.625,1.0,0.074,0.022,0.145
+TiRex,Moirai-2.0,0.875,0.625,1.0,0.093,0.024,0.173
+TiRex,Chronos-Bolt,0.875,0.625,1.0,0.092,0.035,0.162
+TiRex,TabPFN-TS,1.0,1.0,1.0,0.187,0.131,0.263
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+TiRex,Naive,1.0,1.0,1.0,0.253,0.156,0.35
+TiRex,Seasonal Naive,1.0,1.0,1.0,0.447,0.355,0.542
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+AutoETS,TimesFM-2.5,0.875,0.625,1.0,0.061,0.003,0.126
+AutoETS,Moirai-2.0,0.75,0.375,1.0,0.08,0.018,0.149
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+AutoETS,Seasonal Naive,1.0,1.0,1.0,0.439,0.343,0.531
+Toto-1.0,Stat. Ensemble,0.375,0.122,0.75,-0.049,-0.137,0.014
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+Toto-1.0,AutoETS,0.5,0.125,0.875,-0.015,-0.114,0.064
+Toto-1.0,Toto-1.0,0.5,0.5,0.5,0.0,0.0,0.0
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+Toto-1.0,AutoTheta,0.625,0.25,1.0,0.062,-0.023,0.129
+Toto-1.0,TimesFM-2.5,0.875,0.688,1.0,0.047,0.012,0.088
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+Toto-1.0,Naive,1.0,1.0,1.0,0.231,0.124,0.348
+Toto-1.0,Seasonal Naive,1.0,1.0,1.0,0.43,0.354,0.51
+Chronos-2,Stat. Ensemble,0.125,0.0,0.375,-0.039,-0.102,0.002
+Chronos-2,TiRex,0.375,0.0,0.75,-0.02,-0.055,0.013
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+Chronos-2,TimesFM-2.5,0.75,0.497,1.0,0.056,-0.007,0.123
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+Chronos-2,Sundial-Base,1.0,1.0,1.0,0.226,0.162,0.282
+Chronos-2,Naive,1.0,1.0,1.0,0.239,0.146,0.325
+Chronos-2,Seasonal Naive,1.0,1.0,1.0,0.436,0.354,0.523
+Drift,Stat. Ensemble,0.0,0.0,0.0,-0.107,-0.158,-0.061
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+Drift,Toto-1.0,0.375,0.0,0.75,-0.055,-0.13,0.029
+Drift,Chronos-2,0.375,0.0,0.75,-0.065,-0.127,-0.004
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+Drift,AutoARIMA,0.5,0.125,0.875,-0.009,-0.116,0.096
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+Drift,TimesFM-2.5,0.375,0.0,0.75,-0.005,-0.061,0.051
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+AutoARIMA,Stat. Ensemble,0.125,0.0,0.375,-0.096,-0.22,-0.022
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+AutoARIMA,Sundial-Base,0.75,0.375,1.0,0.183,0.046,0.304
+AutoARIMA,Naive,0.875,0.625,1.0,0.197,0.073,0.328
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+AutoTheta,Stat. Ensemble,0.0,0.0,0.0,-0.118,-0.175,-0.068
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+AutoTheta,AutoETS,0.25,0.0,0.5,-0.081,-0.132,-0.032
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+AutoTheta,AutoTheta,0.5,0.5,0.5,0.0,0.0,0.0
+AutoTheta,TimesFM-2.5,0.5,0.125,0.875,-0.016,-0.076,0.043
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+AutoTheta,Naive,1.0,1.0,1.0,0.181,0.091,0.276
+AutoTheta,Seasonal Naive,1.0,1.0,1.0,0.393,0.302,0.487
+TimesFM-2.5,Stat. Ensemble,0.0,0.0,0.0,-0.101,-0.171,-0.05
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+TimesFM-2.5,Seasonal Naive,1.0,1.0,1.0,0.402,0.33,0.473
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+Moirai-2.0,Seasonal Naive,1.0,1.0,1.0,0.39,0.308,0.462
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+Chronos-Bolt,Chronos-Bolt,0.5,0.5,0.5,0.0,0.0,0.0
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+Chronos-Bolt,Naive,1.0,1.0,1.0,0.178,0.1,0.255
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+TabPFN-TS,TiRex,0.0,0.0,0.0,-0.23,-0.357,-0.151
+TabPFN-TS,AutoETS,0.0,0.0,0.0,-0.212,-0.335,-0.129
+TabPFN-TS,Toto-1.0,0.125,0.0,0.375,-0.194,-0.391,-0.035
+TabPFN-TS,Chronos-2,0.0,0.0,0.0,-0.206,-0.355,-0.11
+TabPFN-TS,Drift,0.375,0.0,0.75,-0.133,-0.257,-0.032
+TabPFN-TS,AutoARIMA,0.125,0.0,0.375,-0.143,-0.298,0.024
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+TabPFN-TS,Moirai-2.0,0.25,0.0,0.625,-0.115,-0.289,0.032
+TabPFN-TS,Chronos-Bolt,0.25,0.0,0.625,-0.117,-0.282,0.018
+TabPFN-TS,TabPFN-TS,0.5,0.5,0.5,0.0,0.0,0.0
+TabPFN-TS,Sundial-Base,0.5,0.125,0.875,0.066,-0.055,0.176
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+TabPFN-TS,Seasonal Naive,1.0,1.0,1.0,0.32,0.172,0.453
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+Naive,TiRex,0.0,0.0,0.0,-0.339,-0.537,-0.185
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+Naive,Drift,0.0,0.0,0.0,-0.233,-0.389,-0.118
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+Naive,Seasonal Naive,0.938,0.812,1.0,0.259,0.172,0.333
+Seasonal Naive,Stat. Ensemble,0.0,0.0,0.0,-0.842,-1.212,-0.582
+Seasonal Naive,TiRex,0.0,0.0,0.0,-0.808,-1.182,-0.551
+Seasonal Naive,AutoETS,0.0,0.0,0.0,-0.781,-1.13,-0.523
+Seasonal Naive,Toto-1.0,0.0,0.0,0.0,-0.756,-1.042,-0.548
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+Seasonal Naive,Drift,0.0,0.0,0.0,-0.665,-0.963,-0.455
+Seasonal Naive,AutoARIMA,0.0,0.0,0.0,-0.68,-0.933,-0.504
+Seasonal Naive,AutoTheta,0.0,0.0,0.0,-0.647,-0.95,-0.433
+Seasonal Naive,TimesFM-2.5,0.0,0.0,0.0,-0.673,-0.899,-0.493
+Seasonal Naive,Moirai-2.0,0.0,0.0,0.0,-0.639,-0.86,-0.445
+Seasonal Naive,Chronos-Bolt,0.0,0.0,0.0,-0.641,-0.835,-0.478
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+Seasonal Naive,Naive,0.062,0.0,0.188,-0.35,-0.5,-0.207
+Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0

tables/domain_econ/pairwise_SQL.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
+TiRex,Chronos-2,0.75,0.375,1.0,0.019,-0.006,0.042
+TiRex,Toto-1.0,0.5,0.125,0.875,0.035,-0.046,0.121
+TiRex,Stat. Ensemble,0.875,0.625,1.0,0.034,-0.005,0.066
+TiRex,AutoETS,0.875,0.625,1.0,0.059,0.031,0.083
+TiRex,TimesFM-2.5,1.0,1.0,1.0,0.089,0.048,0.145
+TiRex,Moirai-2.0,0.875,0.625,1.0,0.102,0.039,0.177
+TiRex,Chronos-Bolt,0.875,0.625,1.0,0.088,0.037,0.151
+TiRex,AutoARIMA,0.875,0.625,1.0,0.13,0.037,0.246
+TiRex,Drift,1.0,1.0,1.0,0.144,0.1,0.19
+TiRex,AutoTheta,1.0,1.0,1.0,0.156,0.105,0.209
+TiRex,TabPFN-TS,1.0,1.0,1.0,0.159,0.116,0.216
+TiRex,Sundial-Base,1.0,1.0,1.0,0.298,0.237,0.361
+TiRex,Naive,1.0,1.0,1.0,0.301,0.212,0.389
+TiRex,Seasonal Naive,1.0,1.0,1.0,0.459,0.384,0.538
+Chronos-2,TiRex,0.25,0.0,0.625,-0.02,-0.044,0.006
+Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
+Chronos-2,Toto-1.0,0.5,0.125,0.875,0.016,-0.071,0.099
+Chronos-2,Stat. Ensemble,0.875,0.625,1.0,0.015,-0.034,0.053
+Chronos-2,AutoETS,0.75,0.375,1.0,0.04,0.01,0.068
+Chronos-2,TimesFM-2.5,0.875,0.625,1.0,0.071,0.027,0.126
+Chronos-2,Moirai-2.0,1.0,1.0,1.0,0.085,0.03,0.157
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tables/domain_econ/pairwise_WAPE.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
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+Toto-1.0,TiRex,0.5,0.125,0.875,-0.033,-0.113,0.034
+Toto-1.0,Toto-1.0,0.5,0.5,0.5,0.0,0.0,0.0
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+Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0

tables/domain_econ/pairwise_WQL.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
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+TiRex,TimesFM-2.5,0.875,0.625,1.0,0.049,0.013,0.083
+TiRex,Moirai-2.0,0.75,0.375,1.0,0.116,0.02,0.244
+TiRex,Chronos-Bolt,0.875,0.625,1.0,0.089,0.026,0.167
+TiRex,AutoETS,1.0,1.0,1.0,0.11,0.065,0.159
+TiRex,TabPFN-TS,1.0,1.0,1.0,0.141,0.085,0.196
+TiRex,Stat. Ensemble,1.0,1.0,1.0,0.141,0.083,0.218
+TiRex,AutoARIMA,0.875,0.625,1.0,0.185,0.084,0.309
+TiRex,Drift,1.0,1.0,1.0,0.28,0.184,0.38
+TiRex,AutoTheta,1.0,1.0,1.0,0.288,0.183,0.394
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tables/domain_energy/leaderboard_MASE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,91.20879120879124,35.398352594765115,0.0,3.4626783322727275,0.0,0.0
+TiRex,75.41208791208793,25.111125985019022,0.0,1.3772318233333332,0.038461538461538464,0.0
+TimesFM-2.5,74.17582417582418,24.470577245081294,0.0,36.094269344715904,0.15384615384615385,0.0
+Chronos-Bolt,70.05494505494507,24.79493117234646,0.0,1.1812673024242424,0.0,0.0
+TabPFN-TS,67.03296703296704,28.931010322468566,0.0,213.1836282907102,0.0,0.0
+Moirai-2.0,65.65934065934066,23.276855755353097,0.0,2.6483478834375003,0.3076923076923077,0.0
+Sundial-Base,64.69780219780219,25.973734598235776,0.0,8.734264116875,0.038461538461538464,0.0
+Toto-1.0,64.56043956043956,22.175386726803193,0.0,64.5943206675,0.15384615384615385,0.0
+Stat. Ensemble,39.83516483516484,5.123802463450922,0.0,2087.2821065800895,0.0,15.384615384615385
+AutoARIMA,36.53846153846153,3.34881134283036,0.0,1914.2135565203125,0.0,15.384615384615385
+Seasonal Naive,30.35714285714285,0.0,0.0,1.0154424039285712,0.0,0.0
+AutoTheta,28.57142857142857,1.1815957493498508,0.0,6.5463739134469705,0.0,0.0
+AutoETS,17.857142857142858,-34.975498607867614,0.0,13.10401027465909,0.0,0.0
+Naive,17.44505494505494,-44.77392099300393,0.0,1.012792872857143,0.0,0.0
+Drift,6.593406593406594,-52.299123287549286,0.0,1.0261745340625001,0.0,0.0

tables/domain_energy/leaderboard_SQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,93.13186813186815,43.69603844728434,0.0,3.4626783322727275,0.0,0.0
+TiRex,82.28021978021978,34.44791487661235,0.0,1.3772318233333332,0.038461538461538464,0.0
+TimesFM-2.5,75.82417582417582,33.04019456805117,0.0,36.094269344715904,0.15384615384615385,0.0
+TabPFN-TS,73.62637362637363,38.076295973948326,0.0,213.1836282907102,0.0,0.0
+Chronos-Bolt,70.6043956043956,32.88168947733126,0.0,1.1812673024242424,0.0,0.0
+Moirai-2.0,68.13186813186815,31.522824785428526,0.0,2.6483478834375003,0.3076923076923077,0.0
+Toto-1.0,67.03296703296705,31.075422556050736,0.0,64.5943206675,0.15384615384615385,0.0
+Sundial-Base,55.35714285714286,28.52842382801387,0.0,8.734264116875,0.038461538461538464,0.0
+AutoARIMA,39.28571428571428,9.387893004882919,0.0,1914.2135565203125,0.0,15.384615384615385
+Stat. Ensemble,37.08791208791208,7.135433823240489,0.0,2087.2821065800895,0.0,15.384615384615385
+Seasonal Naive,25.96153846153846,0.0,0.0,1.0154424039285712,0.0,0.0
+AutoETS,24.725274725274723,-30.21022947475347,0.0,13.10401027465909,0.0,0.0
+AutoTheta,20.05494505494506,-10.643501735785609,0.0,6.5463739134469705,0.0,0.0
+Naive,13.873626373626374,-60.57154436477256,0.0,1.012792872857143,0.0,0.0
+Drift,3.0219780219780215,-67.3016516015588,0.0,1.0261745340625001,0.0,0.0

tables/domain_energy/leaderboard_WAPE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,88.73626373626375,37.55790132014533,0.0,3.4626783322727275,0.0,0.0
+TimesFM-2.5,69.78021978021978,26.657949622489184,0.0,36.094269344715904,0.15384615384615385,0.0
+TiRex,69.36813186813188,27.32266216368823,0.0,1.3772318233333332,0.038461538461538464,0.0
+Chronos-Bolt,65.93406593406594,26.60685234340968,0.0,1.1812673024242424,0.0,0.0
+TabPFN-TS,65.38461538461539,30.329432122077236,0.0,213.1836282907102,0.0,0.0
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+Naive,27.884615384615387,-29.595393684781012,0.0,1.012792872857143,0.0,0.0
+Seasonal Naive,27.609890109890113,0.0,0.0,1.0154424039285712,0.0,0.0
+AutoETS,27.472527472527474,-24.061974944434183,0.0,13.10401027465909,0.0,0.0
+Drift,16.75824175824176,-36.91555350097882,0.0,1.0261745340625001,0.0,0.0

tables/domain_energy/leaderboard_WQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,93.6813186813187,45.99295406512989,0.0,3.4626783322727275,0.0,0.0
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+TabPFN-TS,75.54945054945054,39.82364919499898,0.0,213.1836282907102,0.0,0.0
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+Drift,5.769230769230769,-57.44553174606462,0.0,1.0261745340625001,0.0,0.0

tables/domain_energy/pairwise_MASE.csv

@@ -0,0 +1,226 @@
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+AutoARIMA,Seasonal Naive,0.654,0.481,0.808,0.033,0.006,0.059
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+Seasonal Naive,Chronos-2,0.038,0.0,0.115,-0.548,-0.729,-0.384
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+Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/domain_energy/pairwise_SQL.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
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tables/domain_energy/pairwise_WAPE.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
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+AutoARIMA,Moirai-2.0,0.192,0.077,0.346,-0.314,-0.489,-0.175
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+Naive,Chronos-2,0.115,0.0,0.269,-1.075,-1.586,-0.674
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+Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/domain_energy/pairwise_WQL.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
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+Chronos-2,TabPFN-TS,0.808,0.654,0.962,0.103,0.043,0.165
+Chronos-2,TimesFM-2.5,0.808,0.654,0.962,0.159,0.086,0.224
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+Chronos-2,Sundial-Base,0.962,0.885,1.0,0.212,0.16,0.273
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+Chronos-2,AutoETS,0.962,0.885,1.0,0.565,0.464,0.652
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+Chronos-2,Naive,1.0,1.0,1.0,0.642,0.566,0.711
+Chronos-2,Drift,1.0,1.0,1.0,0.657,0.583,0.723
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+TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
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+Sundial-Base,Toto-1.0,0.288,0.135,0.443,-0.033,-0.133,0.06
+Sundial-Base,Sundial-Base,0.5,0.5,0.5,0.0,0.0,0.0
+Sundial-Base,Stat. Ensemble,0.769,0.577,0.923,0.24,0.14,0.328
+Sundial-Base,AutoARIMA,0.808,0.654,0.962,0.24,0.142,0.324
+Sundial-Base,AutoETS,0.846,0.692,0.962,0.448,0.328,0.539
+Sundial-Base,Seasonal Naive,0.885,0.731,1.0,0.314,0.227,0.393
+Sundial-Base,AutoTheta,0.923,0.808,1.0,0.346,0.251,0.424
+Sundial-Base,Naive,0.962,0.885,1.0,0.546,0.446,0.638
+Sundial-Base,Drift,1.0,1.0,1.0,0.564,0.467,0.653
+Stat. Ensemble,Chronos-2,0.0,0.0,0.0,-0.67,-0.887,-0.475
+Stat. Ensemble,TiRex,0.077,0.0,0.192,-0.432,-0.61,-0.286
+Stat. Ensemble,TabPFN-TS,0.038,0.0,0.115,-0.499,-0.711,-0.324
+Stat. Ensemble,TimesFM-2.5,0.038,0.0,0.115,-0.404,-0.576,-0.265
+Stat. Ensemble,Chronos-Bolt,0.077,0.0,0.192,-0.395,-0.558,-0.261
+Stat. Ensemble,Moirai-2.0,0.115,0.0,0.232,-0.378,-0.563,-0.221
+Stat. Ensemble,Toto-1.0,0.154,0.038,0.308,-0.359,-0.531,-0.214
+Stat. Ensemble,Sundial-Base,0.231,0.077,0.423,-0.315,-0.489,-0.162
+Stat. Ensemble,Stat. Ensemble,0.5,0.5,0.5,0.0,0.0,0.0
+Stat. Ensemble,AutoARIMA,0.5,0.308,0.692,0.001,-0.03,0.03
+Stat. Ensemble,AutoETS,0.808,0.654,0.923,0.274,0.156,0.394
+Stat. Ensemble,Seasonal Naive,0.808,0.673,0.923,0.098,0.062,0.134
+Stat. Ensemble,AutoTheta,0.846,0.692,0.962,0.14,0.054,0.228
+Stat. Ensemble,Naive,0.962,0.885,1.0,0.403,0.302,0.5
+Stat. Ensemble,Drift,0.962,0.885,1.0,0.427,0.332,0.521
+AutoARIMA,Chronos-2,0.0,0.0,0.0,-0.672,-0.901,-0.469
+AutoARIMA,TiRex,0.038,0.0,0.115,-0.433,-0.614,-0.285
+AutoARIMA,TabPFN-TS,0.115,0.0,0.269,-0.5,-0.719,-0.316
+AutoARIMA,TimesFM-2.5,0.0,0.0,0.0,-0.405,-0.574,-0.269
+AutoARIMA,Chronos-Bolt,0.077,0.0,0.192,-0.396,-0.568,-0.256
+AutoARIMA,Moirai-2.0,0.077,0.0,0.192,-0.379,-0.569,-0.224
+AutoARIMA,Toto-1.0,0.115,0.0,0.269,-0.36,-0.54,-0.209
+AutoARIMA,Sundial-Base,0.192,0.038,0.346,-0.316,-0.48,-0.166
+AutoARIMA,Stat. Ensemble,0.5,0.308,0.692,-0.001,-0.031,0.029
+AutoARIMA,AutoARIMA,0.5,0.5,0.5,0.0,0.0,0.0
+AutoARIMA,AutoETS,0.731,0.538,0.885,0.273,0.152,0.393
+AutoARIMA,Seasonal Naive,0.846,0.712,0.962,0.097,0.065,0.133
+AutoARIMA,AutoTheta,0.846,0.692,0.962,0.139,0.048,0.225
+AutoARIMA,Naive,0.885,0.769,1.0,0.402,0.299,0.502
+AutoARIMA,Drift,0.923,0.808,1.0,0.427,0.331,0.523
+AutoETS,Chronos-2,0.038,0.0,0.115,-1.301,-1.876,-0.865
+AutoETS,TiRex,0.038,0.0,0.115,-0.973,-1.463,-0.611
+AutoETS,TabPFN-TS,0.038,0.0,0.115,-1.065,-1.575,-0.661
+AutoETS,TimesFM-2.5,0.038,0.0,0.115,-0.934,-1.389,-0.604
+AutoETS,Chronos-Bolt,0.038,0.0,0.115,-0.922,-1.378,-0.581
+AutoETS,Moirai-2.0,0.077,0.0,0.192,-0.898,-1.383,-0.547
+AutoETS,Toto-1.0,0.115,0.0,0.269,-0.873,-1.342,-0.522
+AutoETS,Sundial-Base,0.154,0.038,0.308,-0.812,-1.168,-0.489
+AutoETS,Stat. Ensemble,0.192,0.077,0.346,-0.378,-0.651,-0.185
+AutoETS,AutoARIMA,0.269,0.115,0.462,-0.376,-0.647,-0.18
+AutoETS,AutoETS,0.5,0.5,0.5,0.0,0.0,0.0
+AutoETS,Seasonal Naive,0.423,0.231,0.615,-0.243,-0.498,-0.059
+AutoETS,AutoTheta,0.423,0.231,0.615,-0.185,-0.496,0.024
+AutoETS,Naive,0.692,0.5,0.846,0.177,-0.005,0.343
+AutoETS,Drift,0.769,0.614,0.923,0.211,0.031,0.372
+Seasonal Naive,Chronos-2,0.0,0.0,0.0,-0.852,-1.093,-0.622
+Seasonal Naive,TiRex,0.0,0.0,0.0,-0.588,-0.782,-0.427
+Seasonal Naive,TabPFN-TS,0.0,0.0,0.0,-0.662,-0.896,-0.473
+Seasonal Naive,TimesFM-2.5,0.0,0.0,0.0,-0.556,-0.734,-0.404
+Seasonal Naive,Chronos-Bolt,0.0,0.0,0.0,-0.547,-0.716,-0.403
+Seasonal Naive,Moirai-2.0,0.077,0.0,0.192,-0.528,-0.727,-0.362
+Seasonal Naive,Toto-1.0,0.038,0.0,0.115,-0.507,-0.7,-0.348
+Seasonal Naive,Sundial-Base,0.115,0.0,0.269,-0.458,-0.646,-0.293
+Seasonal Naive,Stat. Ensemble,0.192,0.077,0.327,-0.109,-0.155,-0.066
+Seasonal Naive,AutoARIMA,0.154,0.038,0.288,-0.108,-0.153,-0.069
+Seasonal Naive,AutoETS,0.577,0.385,0.769,0.195,0.055,0.332
+Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0
+Seasonal Naive,AutoTheta,0.5,0.308,0.692,0.047,-0.058,0.151
+Seasonal Naive,Naive,0.75,0.596,0.885,0.338,0.225,0.448
+Seasonal Naive,Drift,0.846,0.692,0.962,0.365,0.257,0.469
+AutoTheta,Chronos-2,0.0,0.0,0.0,-0.942,-1.236,-0.714
+AutoTheta,TiRex,0.0,0.0,0.0,-0.665,-0.895,-0.491
+AutoTheta,TabPFN-TS,0.0,0.0,0.0,-0.743,-1.051,-0.506
+AutoTheta,TimesFM-2.5,0.0,0.0,0.0,-0.633,-0.887,-0.449
+AutoTheta,Chronos-Bolt,0.0,0.0,0.0,-0.622,-0.874,-0.442
+AutoTheta,Moirai-2.0,0.077,0.0,0.192,-0.602,-0.84,-0.415
+AutoTheta,Toto-1.0,0.038,0.0,0.115,-0.581,-0.819,-0.392
+AutoTheta,Sundial-Base,0.077,0.0,0.192,-0.53,-0.735,-0.335
+AutoTheta,Stat. Ensemble,0.154,0.038,0.308,-0.163,-0.296,-0.057
+AutoTheta,AutoARIMA,0.154,0.038,0.308,-0.162,-0.29,-0.05
+AutoTheta,AutoETS,0.577,0.385,0.769,0.156,-0.025,0.332
+AutoTheta,Seasonal Naive,0.5,0.308,0.692,-0.049,-0.178,0.054
+AutoTheta,AutoTheta,0.5,0.5,0.5,0.0,0.0,0.0
+AutoTheta,Naive,0.692,0.5,0.846,0.305,0.169,0.434
+AutoTheta,Drift,0.769,0.615,0.923,0.334,0.2,0.458
+Naive,Chronos-2,0.0,0.0,0.0,-1.796,-2.464,-1.302
+Naive,TiRex,0.0,0.0,0.0,-1.397,-1.954,-0.985
+Naive,TabPFN-TS,0.0,0.0,0.0,-1.509,-2.175,-1.028
+Naive,TimesFM-2.5,0.0,0.0,0.0,-1.35,-1.917,-0.935
+Naive,Chronos-Bolt,0.0,0.0,0.0,-1.335,-1.866,-0.941
+Naive,Moirai-2.0,0.038,0.0,0.115,-1.306,-1.84,-0.93
+Naive,Toto-1.0,0.038,0.0,0.115,-1.275,-1.776,-0.904
+Naive,Sundial-Base,0.038,0.0,0.115,-1.201,-1.761,-0.804
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+Naive,AutoARIMA,0.115,0.0,0.231,-0.672,-1.007,-0.426
+Naive,AutoETS,0.308,0.154,0.5,-0.215,-0.523,0.005
+Naive,Seasonal Naive,0.25,0.115,0.404,-0.51,-0.811,-0.291
+Naive,AutoTheta,0.308,0.154,0.5,-0.439,-0.768,-0.203
+Naive,Naive,0.5,0.5,0.5,0.0,0.0,0.0
+Naive,Drift,0.962,0.885,1.0,0.041,0.026,0.058
+Drift,Chronos-2,0.0,0.0,0.0,-1.915,-2.609,-1.396
+Drift,TiRex,0.0,0.0,0.0,-1.5,-2.068,-1.08
+Drift,TabPFN-TS,0.0,0.0,0.0,-1.616,-2.276,-1.108
+Drift,TimesFM-2.5,0.0,0.0,0.0,-1.45,-2.03,-1.028
+Drift,Chronos-Bolt,0.0,0.0,0.0,-1.435,-1.97,-1.024
+Drift,Moirai-2.0,0.0,0.0,0.0,-1.405,-1.953,-1.008
+Drift,Toto-1.0,0.038,0.0,0.115,-1.372,-1.889,-0.986
+Drift,Sundial-Base,0.0,0.0,0.0,-1.296,-1.882,-0.876
+Drift,Stat. Ensemble,0.038,0.0,0.115,-0.746,-1.089,-0.497
+Drift,AutoARIMA,0.077,0.0,0.192,-0.744,-1.097,-0.495
+Drift,AutoETS,0.231,0.077,0.386,-0.267,-0.592,-0.032
+Drift,Seasonal Naive,0.154,0.038,0.308,-0.574,-0.885,-0.347
+Drift,AutoTheta,0.231,0.077,0.385,-0.501,-0.843,-0.25
+Drift,Naive,0.038,0.0,0.115,-0.043,-0.062,-0.027
+Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/domain_health/leaderboard_MASE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,74.99999999999999,31.785976470244737,0.0,0.33713188125000004,0.0,0.0
+TimesFM-2.5,74.99999999999999,32.87474900010455,0.0,1.1526229114583333,0.0,0.0
+TiRex,70.0,32.475887751126365,0.0,0.3170949572916667,0.0,0.0
+TabPFN-TS,68.57142857142857,30.065972963196263,0.0,28.343891684635416,0.0,0.0
+Moirai-2.0,65.35714285714286,33.79488514595498,0.0,0.34801128166666667,0.1,0.0
+Toto-1.0,60.71428571428571,31.61987589396246,0.0,9.245004833333333,0.0,0.0
+Chronos-Bolt,56.785714285714285,30.041136141125392,0.0,0.406559469375,0.0,0.0
+Stat. Ensemble,50.71428571428572,29.22957519821191,0.0,193.737679803125,0.0,0.0
+AutoETS,49.28571428571429,28.071655389223615,0.0,2.600062978125,0.0,0.0
+Sundial-Base,48.57142857142857,22.707876070444478,0.0,8.29280267,0.0,0.0
+AutoARIMA,36.42857142857142,5.881738788340107,0.0,7.909155262500001,0.0,0.0
+AutoTheta,30.71428571428571,15.209167110886634,0.0,2.196897983333333,0.0,0.0
+Seasonal Naive,21.78571428571429,0.0,0.0,1.1288162752343749,0.0,0.0
+Naive,21.071428571428573,0.30982946320681215,0.0,1.196338605,0.0,0.0
+Drift,20.0,7.491051478030797,0.0,1.1894594236458333,0.0,0.0

tables/domain_health/leaderboard_SQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,81.42857142857143,38.088755291215016,0.0,0.33713188125000004,0.0,0.0
+TimesFM-2.5,76.42857142857142,38.03594076932182,0.0,1.1526229114583333,0.0,0.0
+TiRex,70.71428571428572,36.908809578202614,0.0,0.3170949572916667,0.0,0.0
+Moirai-2.0,69.64285714285714,38.69101656633476,0.0,0.34801128166666667,0.1,0.0
+Toto-1.0,66.42857142857143,37.84147796075432,0.0,9.245004833333333,0.0,0.0
+TabPFN-TS,65.71428571428571,35.21500006276798,0.0,28.343891684635416,0.0,0.0
+Chronos-Bolt,61.07142857142858,36.49963485983284,0.0,0.406559469375,0.0,0.0
+AutoETS,52.85714285714287,31.12404362920507,0.0,2.600062978125,0.0,0.0
+Stat. Ensemble,49.285714285714285,31.66665055693333,0.0,193.737679803125,0.0,0.0
+Sundial-Base,38.57142857142858,23.555776015856367,0.0,8.29280267,0.0,0.0
+AutoARIMA,37.857142857142854,8.155432249884543,0.0,7.909155262500001,0.0,0.0
+AutoTheta,26.42857142857143,17.733419423817608,0.0,2.196897983333333,0.0,0.0
+Seasonal Naive,21.071428571428573,0.0,0.0,1.1288162752343749,0.0,0.0
+Naive,17.5,-7.297793633399574,0.0,1.196338605,0.0,0.0
+Drift,15.0,0.4041900778196994,0.0,1.1894594236458333,0.0,0.0

tables/domain_health/leaderboard_WAPE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+TimesFM-2.5,72.14285714285714,30.23643324970241,0.0,1.1526229114583333,0.0,0.0
+TiRex,67.14285714285715,29.759653338440085,0.0,0.3170949572916667,0.0,0.0
+Chronos-2,67.14285714285714,29.12048426647892,0.0,0.33713188125000004,0.0,0.0
+Moirai-2.0,66.07142857142858,31.59592125809332,0.0,0.34801128166666667,0.1,0.0
+Toto-1.0,64.2857142857143,28.503622669499972,0.0,9.245004833333333,0.0,0.0
+Stat. Ensemble,57.85714285714286,29.695571630620133,0.0,193.737679803125,0.0,0.0
+TabPFN-TS,57.85714285714286,22.16773042840192,0.0,28.343891684635416,0.0,0.0
+Chronos-Bolt,57.50000000000001,25.88326333574207,0.0,0.406559469375,0.0,0.0
+AutoETS,57.14285714285714,29.452418664318316,0.0,2.600062978125,0.0,0.0
+Sundial-Base,47.14285714285714,16.462367071514482,0.0,8.29280267,0.0,0.0
+AutoARIMA,35.0,15.909991992310612,0.0,7.909155262500001,0.0,0.0
+AutoTheta,30.0,14.265515884511814,0.0,2.196897983333333,0.0,0.0
+Naive,24.642857142857146,0.09410339815076885,0.0,1.196338605,0.0,0.0
+Seasonal Naive,23.92857142857143,0.0,0.0,1.1288162752343749,0.0,0.0
+Drift,22.142857142857146,6.9951032330727525,0.0,1.1894594236458333,0.0,0.0

tables/domain_health/leaderboard_WQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+TimesFM-2.5,74.28571428571428,40.552489915488046,0.0,1.1526229114583333,0.0,0.0
+Chronos-2,73.57142857142857,40.42137769720644,0.0,0.33713188125000004,0.0,0.0
+Moirai-2.0,71.78571428571428,41.64215769175597,0.0,0.34801128166666667,0.1,0.0
+TiRex,67.85714285714286,38.88592211612176,0.0,0.3170949572916667,0.0,0.0
+Toto-1.0,64.28571428571429,40.23878103293733,0.0,9.245004833333333,0.0,0.0
+Chronos-Bolt,61.78571428571429,38.083893012167536,0.0,0.406559469375,0.0,0.0
+TabPFN-TS,57.85714285714286,34.096239445747244,0.0,28.343891684635416,0.0,0.0
+Stat. Ensemble,57.857142857142854,33.50700504489347,0.0,193.737679803125,0.0,0.0
+AutoETS,52.85714285714286,32.19312845196244,0.0,2.600062978125,0.0,0.0
+AutoARIMA,41.42857142857143,19.92503103258492,0.0,7.909155262500001,0.0,0.0
+Sundial-Base,39.28571428571429,24.248203686038707,0.0,8.29280267,0.0,0.0
+AutoTheta,29.999999999999993,18.14389549105302,0.0,2.196897983333333,0.0,0.0
+Seasonal Naive,21.071428571428577,0.0,0.0,1.1288162752343749,0.0,0.0
+Drift,18.571428571428577,0.8421348585152089,0.0,1.1894594236458333,0.0,0.0
+Naive,17.5,-6.552998309491742,0.0,1.196338605,0.0,0.0

tables/domain_health/pairwise_MASE.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
+Chronos-2,TimesFM-2.5,0.5,0.2,0.8,-0.016,-0.124,0.085
+Chronos-2,TiRex,0.6,0.3,0.9,-0.01,-0.052,0.023
+Chronos-2,TabPFN-TS,0.7,0.4,1.0,0.025,-0.082,0.154
+Chronos-2,Moirai-2.0,0.7,0.4,0.9,-0.03,-0.114,0.028
+Chronos-2,Toto-1.0,0.7,0.4,1.0,0.002,-0.046,0.041
+Chronos-2,Chronos-Bolt,0.8,0.6,1.0,0.025,-0.04,0.08
+Chronos-2,Stat. Ensemble,0.6,0.3,0.9,0.036,-0.098,0.168
+Chronos-2,AutoETS,0.6,0.3,0.9,0.052,-0.07,0.182
+Chronos-2,Sundial-Base,0.8,0.6,1.0,0.117,-0.048,0.245
+Chronos-2,AutoARIMA,0.9,0.7,1.0,0.275,0.078,0.425
+Chronos-2,AutoTheta,0.9,0.7,1.0,0.196,0.073,0.308
+Chronos-2,Seasonal Naive,0.9,0.7,1.0,0.318,0.17,0.455
+Chronos-2,Naive,0.9,0.7,1.0,0.316,0.185,0.434
+Chronos-2,Drift,0.9,0.7,1.0,0.263,0.17,0.344
+TimesFM-2.5,Chronos-2,0.5,0.2,0.8,0.016,-0.093,0.11
+TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
+TimesFM-2.5,TiRex,0.7,0.4,0.9,0.006,-0.088,0.075
+TimesFM-2.5,TabPFN-TS,0.6,0.3,0.9,0.04,-0.118,0.212
+TimesFM-2.5,Moirai-2.0,0.6,0.3,0.9,-0.014,-0.094,0.036
+TimesFM-2.5,Toto-1.0,0.5,0.2,0.8,0.018,-0.087,0.112
+TimesFM-2.5,Chronos-Bolt,0.8,0.6,1.0,0.041,-0.026,0.112
+TimesFM-2.5,Stat. Ensemble,0.8,0.6,1.0,0.052,-0.167,0.215
+TimesFM-2.5,AutoETS,0.7,0.4,1.0,0.067,-0.126,0.221
+TimesFM-2.5,Sundial-Base,0.7,0.4,0.9,0.132,0.016,0.262
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tables/domain_health/pairwise_SQL.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
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+Chronos-2,Sundial-Base,0.8,0.6,1.0,0.19,0.034,0.308
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+Chronos-2,AutoTheta,1.0,1.0,1.0,0.247,0.112,0.362
+Chronos-2,Seasonal Naive,1.0,1.0,1.0,0.381,0.242,0.504
+Chronos-2,Naive,1.0,1.0,1.0,0.423,0.295,0.531
+Chronos-2,Drift,1.0,1.0,1.0,0.378,0.281,0.47
+TimesFM-2.5,Chronos-2,0.5,0.2,0.8,-0.001,-0.104,0.084
+TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
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tables/domain_health/pairwise_WAPE.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
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+Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/domain_health/pairwise_WQL.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
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+TimesFM-2.5,Toto-1.0,0.5,0.2,0.8,0.005,-0.109,0.12
+TimesFM-2.5,Chronos-Bolt,0.8,0.6,1.0,0.04,-0.018,0.102
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+TimesFM-2.5,AutoETS,0.6,0.3,0.9,0.123,-0.074,0.306
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tables/domain_mobility/leaderboard_MASE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,90.47619047619048,22.491484624334955,0.0,0.26784322794871795,0.0,0.0
+TiRex,79.76190476190476,21.288270041647138,0.0,0.26115691663789,0.0,0.0
+TimesFM-2.5,72.61904761904762,20.340171911148776,0.0,0.7936244461538462,0.0,0.0
+TabPFN-TS,67.85714285714286,19.296786950554633,0.0,52.37668190762821,0.0,0.0
+Toto-1.0,64.28571428571429,19.57366854063398,0.0,24.157588808205134,0.0,0.0
+Chronos-Bolt,56.54761904761904,16.7501542380026,0.0,0.20589281349358973,0.0,0.0
+Moirai-2.0,55.35714285714285,16.732852664181774,0.0,1.7026751536335043,0.8333333333333334,0.0
+Sundial-Base,51.1904761904762,12.142829552531376,0.0,7.8157010582409505,0.0,0.0
+Stat. Ensemble,44.04761904761906,6.0859849264475745,0.0,28.261405801640862,0.0,33.33333333333333
+AutoTheta,39.28571428571429,5.560116179069641,0.0,1.7578207659544929,0.0,0.0
+AutoARIMA,39.28571428571429,3.511020388587349,0.0,13.813905255526317,0.0,33.33333333333333
+Seasonal Naive,29.166666666666668,0.0,0.0,0.1546446881304136,0.0,0.0
+AutoETS,28.57142857142857,-18.72817524266548,0.0,4.8025043967024885,0.0,0.0
+Drift,17.857142857142854,-30.331940880835084,0.0,0.14687456285464573,0.0,0.0
+Naive,13.690476190476192,-23.849674243208028,0.0,0.15513795943548064,0.0,0.0

tables/domain_mobility/leaderboard_SQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,90.47619047619048,35.58787936204444,0.0,0.26784322794871795,0.0,0.0
+TiRex,82.14285714285714,34.53851918169612,0.0,0.26115691663789,0.0,0.0
+TimesFM-2.5,76.19047619047619,32.48574600837375,0.0,0.7936244461538462,0.0,0.0
+TabPFN-TS,70.23809523809523,30.439643747943656,0.0,52.37668190762821,0.0,0.0
+Toto-1.0,61.90476190476192,31.991168200458866,0.0,24.157588808205134,0.0,0.0
+Chronos-Bolt,60.11904761904761,28.156281905326587,0.0,0.20589281349358973,0.0,0.0
+Moirai-2.0,55.35714285714286,27.784248749062858,0.0,1.7026751536335043,0.8333333333333334,0.0
+AutoARIMA,46.42857142857144,6.61529754700757,0.0,13.813905255526317,0.0,33.33333333333333
+Stat. Ensemble,45.23809523809524,5.453467246975352,0.0,28.261405801640862,0.0,33.33333333333333
+Sundial-Base,39.285714285714285,20.76097880985013,0.0,7.8157010582409505,0.0,0.0
+AutoTheta,35.71428571428573,-11.90147576720375,0.0,1.7578207659544929,0.0,0.0
+Seasonal Naive,30.357142857142865,0.0,0.0,0.1546446881304136,0.0,0.0
+AutoETS,23.80952380952381,-205.05655001362152,0.0,4.8025043967024885,0.0,0.0
+Drift,19.047619047619044,-119.36994598641353,0.0,0.14687456285464573,0.0,0.0
+Naive,13.690476190476192,-118.20697665885001,0.0,0.15513795943548064,0.0,0.0

tables/domain_mobility/leaderboard_WAPE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,83.33333333333334,21.324710763820896,0.0,0.26784322794871795,0.0,0.0
+TiRex,78.57142857142856,20.85333057264023,0.0,0.26115691663789,0.0,0.0
+TimesFM-2.5,72.61904761904762,20.363401785223758,0.0,0.7936244461538462,0.0,0.0
+TabPFN-TS,70.23809523809523,24.025327260859054,0.0,52.37668190762821,0.0,0.0
+Toto-1.0,66.66666666666667,19.95962232581897,0.0,24.157588808205134,0.0,0.0
+Moirai-2.0,56.547619047619044,16.170926676832266,0.0,1.7026751536335043,0.8333333333333334,0.0
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+AutoARIMA,40.476190476190474,6.515361175222156,0.0,13.813905255526317,0.0,33.33333333333333
+AutoTheta,38.0952380952381,11.488387532362754,0.0,1.7578207659544929,0.0,0.0
+Seasonal Naive,32.73809523809524,0.0,0.0,0.1546446881304136,0.0,0.0
+AutoETS,28.57142857142857,-21.961397745126266,0.0,4.8025043967024885,0.0,0.0
+Drift,17.857142857142854,-22.032645241338678,0.0,0.14687456285464573,0.0,0.0
+Naive,16.071428571428573,-23.914241845272176,0.0,0.15513795943548064,0.0,0.0

tables/domain_mobility/leaderboard_WQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,83.33333333333334,35.204274552047885,0.0,0.26784322794871795,0.0,0.0
+TiRex,83.33333333333333,34.7246344700893,0.0,0.26115691663789,0.0,0.0
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+Chronos-Bolt,57.738095238095234,27.880696156865582,0.0,0.20589281349358973,0.0,0.0
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+AutoTheta,36.90476190476191,-6.590864483593606,0.0,1.7578207659544929,0.0,0.0
+Seasonal Naive,32.73809523809524,0.0,0.0,0.1546446881304136,0.0,0.0
+AutoETS,23.80952380952381,-198.1123633616785,0.0,4.8025043967024885,0.0,0.0
+Drift,19.047619047619044,-103.02469270449221,0.0,0.14687456285464573,0.0,0.0
+Naive,16.071428571428573,-115.43969504137333,0.0,0.15513795943548064,0.0,0.0

tables/domain_mobility/pairwise_MASE.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
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tables/domain_mobility/pairwise_SQL.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
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tables/domain_mobility/pairwise_WAPE.csv

@@ -0,0 +1,226 @@
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tables/domain_mobility/pairwise_WQL.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
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+Stat. Ensemble,TabPFN-TS,0.167,0.0,0.5,-0.383,-0.769,-0.125
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+Stat. Ensemble,Seasonal Naive,0.667,0.333,0.917,0.083,-0.124,0.234
+Stat. Ensemble,AutoETS,1.0,1.0,1.0,0.683,0.188,0.925
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+Sundial-Base,Drift,0.833,0.5,1.0,0.6,0.067,0.776
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+AutoTheta,TabPFN-TS,0.167,0.0,0.5,-0.607,-1.589,-0.099
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+AutoTheta,AutoETS,0.667,0.167,1.0,0.654,0.026,0.921
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+AutoTheta,Naive,1.0,1.0,1.0,0.505,0.354,0.614
+Seasonal Naive,Chronos-2,0.167,0.0,0.5,-0.543,-0.975,-0.231
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+Seasonal Naive,AutoARIMA,0.333,0.083,0.667,-0.111,-0.314,0.086
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+Seasonal Naive,Sundial-Base,0.167,0.0,0.5,-0.232,-0.619,0.073
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+Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0
+Seasonal Naive,AutoETS,0.667,0.333,1.0,0.665,0.116,0.923
+Seasonal Naive,Drift,0.833,0.5,1.0,0.507,0.116,0.661
+Seasonal Naive,Naive,0.917,0.75,1.0,0.536,0.351,0.634
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+Naive,Naive,0.5,0.5,0.5,0.0,0.0,0.0

tables/domain_nature/leaderboard_MASE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,87.75510204081634,30.497126962065213,0.0,1.2207885477272729,0.0,0.0
+TiRex,87.75510204081634,28.929993008935707,0.0,0.22086016883116882,0.0,0.0
+TimesFM-2.5,69.3877551020408,27.897712615827363,0.0,3.029942118181818,0.0,0.0
+Chronos-Bolt,65.81632653061223,26.1953757312394,0.0,0.2863284199134199,0.0,0.0
+Toto-1.0,60.204081632653065,23.116505027318457,0.0,22.23460966136364,0.0,0.0
+Stat. Ensemble,54.08163265306123,19.483181611552936,0.0,326.0961104430194,0.0,14.285714285714285
+TabPFN-TS,53.06122448979591,24.364747788672158,0.0,82.75914924545454,0.0,0.0
+Moirai-2.0,51.53061224489795,21.62561301743583,0.0,0.30303544404761906,0.42857142857142855,0.0
+Sundial-Base,51.02040816326531,21.824543473624225,0.0,8.178181660238096,0.0,0.0
+AutoTheta,46.93877551020408,20.695726985214247,0.0,3.516469010952381,0.0,0.0
+AutoARIMA,36.734693877551024,16.580678081858668,0.0,45.50572387840909,0.0,14.285714285714285
+AutoETS,34.69387755102041,-12.099205356952947,0.0,7.976041772727273,0.0,0.0
+Drift,26.530612244897956,7.623192159303982,0.0,0.9546919150000002,0.0,0.0
+Naive,16.3265306122449,5.632467591646206,0.0,1.0228832204761906,0.0,0.0
+Seasonal Naive,8.16326530612245,0.0,0.0,0.9169890385714287,0.0,0.0

tables/domain_nature/leaderboard_SQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+Chronos-2,92.85714285714288,36.382587745113646,0.0,1.2207885477272729,0.0,0.0
+TiRex,91.83673469387756,35.0706905436059,0.0,0.22086016883116882,0.0,0.0
+TimesFM-2.5,70.40816326530611,33.26259738148211,0.0,3.029942118181818,0.0,0.0
+Chronos-Bolt,69.89795918367346,31.785831178509618,0.0,0.2863284199134199,0.0,0.0
+TabPFN-TS,66.32653061224491,30.553466306238843,0.0,82.75914924545454,0.0,0.0
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+Moirai-2.0,55.61224489795919,27.592638035700944,0.0,0.30303544404761906,0.42857142857142855,0.0
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+AutoARIMA,44.89795918367347,18.654282393699383,0.0,45.50572387840909,0.0,14.285714285714285
+Sundial-Base,42.85714285714285,22.97655424697571,0.0,8.178181660238096,0.0,0.0
+AutoETS,28.57142857142857,-100.14545865598201,0.0,7.976041772727273,0.0,0.0
+AutoTheta,26.530612244897956,2.844580213482706,0.0,3.516469010952381,0.0,0.0
+Seasonal Naive,17.346938775510203,0.0,0.0,0.9169890385714287,0.0,0.0
+Drift,16.3265306122449,-14.734289645185616,0.0,0.9546919150000002,0.0,0.0
+Naive,12.244897959183671,-16.315361039765854,0.0,1.0228832204761906,0.0,0.0

tables/domain_nature/leaderboard_WAPE.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+TiRex,79.59183673469387,40.186105154065274,0.0,0.22086016883116882,0.0,0.0
+Chronos-2,78.57142857142857,40.705400799173944,0.0,1.2207885477272729,0.0,0.0
+TimesFM-2.5,72.44897959183673,39.71301301045573,0.0,3.029942118181818,0.0,0.0
+Toto-1.0,71.42857142857142,38.015338187520356,0.0,22.23460966136364,0.0,0.0
+Chronos-Bolt,64.79591836734696,37.474958160131976,0.0,0.2863284199134199,0.0,0.0
+TabPFN-TS,62.244897959183675,37.3159595613183,0.0,82.75914924545454,0.0,0.0
+Stat. Ensemble,53.06122448979591,23.729471306196494,0.0,326.0961104430194,0.0,14.285714285714285
+Moirai-2.0,50.51020408163266,33.75926497739168,0.0,0.30303544404761906,0.42857142857142855,0.0
+AutoTheta,46.938775510204074,32.22717023498179,0.0,3.516469010952381,0.0,0.0
+AutoETS,42.857142857142854,1.699868730786347,0.0,7.976041772727273,0.0,0.0
+AutoARIMA,35.71428571428573,8.157108575448458,0.0,45.50572387840909,0.0,14.285714285714285
+Sundial-Base,35.71428571428572,25.06871812266033,0.0,8.178181660238096,0.0,0.0
+Drift,25.510204081632647,15.503053940810641,0.0,0.9546919150000002,0.0,0.0
+Naive,20.40816326530612,18.858044072466562,0.0,1.0228832204761906,0.0,0.0
+Seasonal Naive,10.20408163265306,0.0,0.0,0.9169890385714287,0.0,0.0

tables/domain_nature/leaderboard_WQL.csv

@@ -0,0 +1,16 @@
+model_name,win_rate,skill_score,median_training_time_s_per100,median_inference_time_s_per100,training_corpus_overlap,num_failures
+TiRex,83.67346938775509,55.845887240463945,0.0,0.22086016883116882,0.0,0.0
+Chronos-2,81.63265306122449,56.29067190301464,0.0,1.2207885477272729,0.0,0.0
+TimesFM-2.5,75.51020408163265,55.93935384077703,0.0,3.029942118181818,0.0,0.0
+Toto-1.0,73.46938775510203,54.33015876380617,0.0,22.23460966136364,0.0,0.0
+Chronos-Bolt,71.9387755102041,53.88724931110583,0.0,0.2863284199134199,0.0,0.0
+TabPFN-TS,68.36734693877553,54.38713321564093,0.0,82.75914924545454,0.0,0.0
+Moirai-2.0,55.61224489795919,51.33483781629431,0.0,0.30303544404761906,0.42857142857142855,0.0
+Stat. Ensemble,53.06122448979592,15.451233242065076,0.0,326.0961104430194,0.0,14.285714285714285
+AutoARIMA,48.979591836734706,20.43246116076548,0.0,45.50572387840909,0.0,14.285714285714285
+Sundial-Base,38.775510204081634,41.11912582586918,0.0,8.178181660238096,0.0,0.0
+AutoETS,29.59183673469387,-93.15158001036382,0.0,7.976041772727273,0.0,0.0
+AutoTheta,23.46938775510204,-9.985676915157615,0.0,3.516469010952381,0.0,0.0
+Seasonal Naive,22.448979591836736,0.0,0.0,0.9169890385714287,0.0,0.0
+Drift,12.244897959183671,-38.89991385039795,0.0,0.9546919150000002,0.0,0.0
+Naive,11.224489795918368,-39.372276784080775,0.0,1.0228832204761906,0.0,0.0

tables/domain_nature/pairwise_MASE.csv

@@ -0,0 +1,226 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
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+Chronos-2,Naive,1.0,1.0,1.0,0.263,0.185,0.334
+Chronos-2,Seasonal Naive,1.0,1.0,1.0,0.305,0.228,0.366
+TiRex,Chronos-2,0.429,0.143,0.857,-0.023,-0.072,0.025
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