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README.md

@@ -1,7 +1,7 @@
 ---
 license: apache-2.0
 pipeline_tag: time-series-forecasting
-library_name: pytorch
+library_name: xgboost
 base_model:
 - amazon/chronos-2
 - google/timesfm-2.5-200m-pytorch
@@ -12,59 +12,66 @@ base_model:
 tags:
 - time-series-forecasting
 - foundation-models
-- pretrained-models
 - time-series
-- timeseries
 - forecasting
 - ensemble
-- meta-learning
 - agentic
 - gift-eval
 - xgboost
-
 ---
 
-> [!WARNING]
-> **This is a benchmarking artifact for the GIFT-Eval submission.**
-> CastStar is an agentic time-series forecasting system built on top of multiple foundation forecasting models. The submitted GIFT-Eval result uses pre-computed forecasts and a meta-selection/ensemble layer over the model pool. It is intended to make the leaderboard submission reproducible and comparable under the GIFT-Eval protocol.
-
-## What This Is
-
-CastStar combines forecasts from a pool of time-series foundation models and uses a learned gating strategy to select or weight base-model predictions for each forecasting setting. The system follows the same general meta-forecasting idea as FFORMA-style model selection, where lightweight time-series features and dataset metadata guide the choice of forecasting experts.
+# CastStar
 
-The GIFT-Eval submission evaluates CastStar on all 97 dataset configurations using the official CSV format.
+CastStar is an agentic time-series forecasting system built on multiple
+forecasting foundation models. This repository contains the frozen inference
+assets for reproducing the CastStar submission on all 97 GIFT-Eval dataset
+configurations.
 
 ## Model Pool
 
-CastStar uses the following base forecasting models. Compared with the Toto-2.0-FnF setup, CastStar only uses the **Toto-2.0-2.5B** checkpoint from the Toto family.
+| Model | Source |
+|---|---|
+| Chronos-2 | [amazon/chronos-2](https://huggingface.co/amazon/chronos-2) |
+| TimesFM-2.5 | [google/timesfm-2.5-200m-pytorch](https://huggingface.co/google/timesfm-2.5-200m-pytorch) |
+| FlowState | [ibm-research/flowstate](https://huggingface.co/ibm-research/flowstate) |
+| TiReX | [NX-AI/TiRex-1.1-gifteval](https://huggingface.co/NX-AI/TiRex-1.1-gifteval) |
+| PatchTST-FM | [ibm-research/patchtst-fm-r1](https://huggingface.co/ibm-research/patchtst-fm-r1) |
+| Toto-2.0-2.5B | [Datadog/Toto-2.0-2.5B](https://huggingface.co/Datadog/Toto-2.0-2.5B) |
 
-|  #   | Model                                                        |  Family   |
-| :--: | ------------------------------------------------------------ | :-------: |
-|  0   | [chronos-2](https://huggingface.co/amazon/chronos-2)         |  Chronos  |
-|  1   | [timesfm-2.5](https://huggingface.co/google/timesfm-2.5-200m-pytorch) |  TimesFM  |
-|  2   | [flowstate](https://huggingface.co/ibm-research/flowstate)   | FlowState |
-|  3   | [tirex](https://huggingface.co/NX-AI/TiRex-1.1-gifteval)     |   TiRex   |
-|  4   | [patchtst-fm](https://huggingface.co/ibm-research/patchtst-fm-r1) | PatchTST  |
-|  5   | [toto-2.0-2.5b](https://huggingface.co/Datadog/Toto-2.0-2.5B) | Toto 2.0  |
+The original model repositories retain their respective licenses and terms.
 
-## Key Features
+## Artifact Layout
 
-- **Agentic forecasting system:** CastStar uses multiple forecasting experts and a decision layer to produce final probabilistic forecasts.
-- **Foundation-model pool:** The model pool includes Chronos-2, TimesFM-2.5, FlowState, TiRex, PatchTST-FM, and Toto-2.0-2.5B.
-- **GIFT-Eval compatible:** Results are submitted in the official GIFT-Eval format with 97/97 dataset configurations and 11 evaluation metrics.
-- **No test-data leakage:** The submitted configuration reports no GIFT-Eval test-data leakage.
-- **Benchmark-focused artifact:** The submitted result is designed for leaderboard evaluation and reproducibility under the GIFT-Eval protocol.
+```text
+CastStar-HF/
+├── README.md
+├── manifest.json
+├── calibration/
+│   └── bucket_class_biases.csv
+├── models/
+│   ├── pre/
+│   └── post/
+└── test/
+    ├── input_features/
+    ├── predictions/
+    └── predicted_features/
+```
 
-## GIFT-Eval Submission
+The bundle contains only the frozen files required by the reproduction
+notebook. It does not contain training data or training code.
 
-The submitted files are:
+## Reproduction
 
-```text
-results/CastStar/all_results.csv
-results/CastStar/config.json
-```
+1. Clone and install [GIFT-Eval](https://github.com/SalesforceAIResearch/gift-eval).
+2. Download the [Salesforce/GiftEval](https://huggingface.co/datasets/Salesforce/GiftEval) dataset.
+3. Open [`notebooks/caststar.ipynb`](https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/caststar.ipynb).
+4. Set the local GIFT-Eval dataset path and run the notebook.
+
+The notebook downloads this repository, runs frozen CastStar inference,
+evaluates the forecasts with the official GIFT-Eval metrics, and writes a
+submission-format CSV.
 
-Submission metadata:
+## GIFT-Eval Submission
 
 ```json
 {
@@ -72,20 +79,14 @@ Submission metadata:
   "model_type": "agentic",
   "model_dtype": "float32",
   "model_link": "https://huggingface.co/USTC-AGI/CastStar",
-  "code_link": "https://github.com/ustc-time-series/CastStar",
-  "org": "CastStar",
+  "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/caststar.ipynb",
+  "org": "USTC-AGI",
   "testdata_leakage": "No",
-  "replication_code_available": "No"
+  "replication_code_available": "Yes"
 }
 ```
 
-## Additional Resources
-
-- [GIFT-Eval benchmark](https://huggingface.co/spaces/Salesforce/GIFT-Eval)
-- [GIFT-Eval GitHub repository](https://github.com/SalesforceAIResearch/gift-eval)
-- [CastStar GitHub repository](https://github.com/ustc-time-series/CastStar)
-- [CastStar model page](https://huggingface.co/USTC-AGI/CastStar)
-
-## Citation
+## Intended Use
 
-If you use CastStar, please cite the corresponding CastStar paper or repository when available.
+These assets are intended for reproducing and inspecting the CastStar
+GIFT-Eval benchmark submission.

calibration/bucket_class_biases.csv

@@ -0,0 +1,121 @@
+bucket,model,bias
+10S|long,toto-2.0-2.5b,-0.8833333333333333
+10S|long,chronos-2,-1.1333333333333333
+10S|long,tirex,0.6666666666666666
+10S|long,patchtst-fm,1.0666666666666667
+10S|long,flowstate,-0.033333333333333326
+10S|long,timesfm-2.5,0.31666666666666665
+10S|medium,toto-2.0-2.5b,-0.4083333333333333
+10S|medium,chronos-2,-1.1583333333333332
+10S|medium,tirex,0.6416666666666666
+10S|medium,patchtst-fm,0.04166666666666663
+10S|medium,flowstate,0.5916666666666667
+10S|medium,timesfm-2.5,0.29166666666666663
+10S|short,toto-2.0-2.5b,-0.050000000000000044
+10S|short,chronos-2,-1.3
+10S|short,tirex,0.49999999999999994
+10S|short,patchtst-fm,-0.10000000000000003
+10S|short,flowstate,0.8
+10S|short,timesfm-2.5,0.14999999999999997
+10T|long,toto-2.0-2.5b,2.1416666666666666
+10T|long,chronos-2,0.46166666666666656
+10T|long,tirex,-3.4883333333333333
+10T|long,patchtst-fm,-0.33833333333333326
+10T|long,flowstate,2.0616666666666665
+10T|long,timesfm-2.5,-0.8383333333333333
+10T|medium,toto-2.0-2.5b,-1.1366666666666667
+10T|medium,chronos-2,0.8633333333333333
+10T|medium,tirex,-1.8366666666666667
+10T|medium,patchtst-fm,1.4833333333333334
+10T|medium,flowstate,0.8133333333333332
+10T|medium,timesfm-2.5,-0.18666666666666673
+10T|short,toto-2.0-2.5b,0.11666666666666664
+10T|short,chronos-2,-1.1333333333333333
+10T|short,tirex,0.6666666666666666
+10T|short,patchtst-fm,0.06666666666666665
+10T|short,flowstate,-0.033333333333333326
+10T|short,timesfm-2.5,0.31666666666666665
+15T|long,toto-2.0-2.5b,-0.31666666666666665
+15T|long,chronos-2,0.7833333333333334
+15T|long,tirex,0.9333333333333333
+15T|long,patchtst-fm,-0.5166666666666666
+15T|long,flowstate,-0.6166666666666667
+15T|long,timesfm-2.5,-0.26666666666666666
+15T|medium,toto-2.0-2.5b,0.6916666666666667
+15T|medium,chronos-2,-1.5383333333333333
+15T|medium,tirex,1.2616666666666667
+15T|medium,patchtst-fm,0.11166666666666665
+15T|medium,flowstate,-0.43833333333333335
+15T|medium,timesfm-2.5,-0.08833333333333336
+15T|short,toto-2.0-2.5b,-0.11333333333333329
+15T|short,chronos-2,0.3866666666666667
+15T|short,tirex,-0.31333333333333335
+15T|short,patchtst-fm,0.33666666666666667
+15T|short,flowstate,-0.38333333333333336
+15T|short,timesfm-2.5,0.08666666666666667
+5T|long,toto-2.0-2.5b,-0.62
+5T|long,chronos-2,-0.7000000000000001
+5T|long,tirex,-0.17000000000000015
+5T|long,patchtst-fm,-0.6200000000000002
+5T|long,flowstate,1.38
+5T|long,timesfm-2.5,0.7299999999999999
+5T|medium,toto-2.0-2.5b,-0.5216666666666667
+5T|medium,chronos-2,-2.0516666666666667
+5T|medium,tirex,-0.7016666666666667
+5T|medium,patchtst-fm,1.4783333333333333
+5T|medium,flowstate,1.3483333333333332
+5T|medium,timesfm-2.5,0.4483333333333332
+5T|short,toto-2.0-2.5b,0.11666666666666664
+5T|short,chronos-2,-1.1333333333333333
+5T|short,tirex,0.6666666666666666
+5T|short,patchtst-fm,0.06666666666666665
+5T|short,flowstate,-0.033333333333333326
+5T|short,timesfm-2.5,0.31666666666666665
+A|short,toto-2.0-2.5b,-0.44499999999999995
+A|short,chronos-2,-1.6750000000000003
+A|short,tirex,-0.8749999999999999
+A|short,patchtst-fm,0.795
+A|short,flowstate,1.425
+A|short,timesfm-2.5,0.775
+D|short,toto-2.0-2.5b,0.2383333333333334
+D|short,chronos-2,-0.5216666666666665
+D|short,tirex,0.08833333333333337
+D|short,patchtst-fm,-0.3316666666666666
+D|short,flowstate,0.08833333333333343
+D|short,timesfm-2.5,0.4383333333333334
+H|long,toto-2.0-2.5b,-0.7166666666666666
+H|long,chronos-2,-0.9666666666666666
+H|long,tirex,0.8333333333333333
+H|long,patchtst-fm,0.23333333333333334
+H|long,flowstate,0.13333333333333336
+H|long,timesfm-2.5,0.48333333333333334
+H|medium,toto-2.0-2.5b,1.25
+H|medium,chronos-2,-2.4
+H|medium,tirex,1.2
+H|medium,patchtst-fm,0.6
+H|medium,flowstate,-1.25
+H|medium,timesfm-2.5,0.6
+H|short,toto-2.0-2.5b,1.0383333333333336
+H|short,chronos-2,-1.7416666666666665
+H|short,tirex,1.5283333333333335
+H|short,patchtst-fm,-0.9916666666666665
+H|short,flowstate,-1.0916666666666666
+H|short,timesfm-2.5,1.2583333333333335
+M|short,toto-2.0-2.5b,-0.9766666666666668
+M|short,chronos-2,0.5033333333333334
+M|short,tirex,-1.0966666666666667
+M|short,patchtst-fm,0.21333333333333326
+M|short,flowstate,0.3533333333333333
+M|short,timesfm-2.5,1.0033333333333334
+Q|short,toto-2.0-2.5b,0.53
+Q|short,chronos-2,-1.32
+Q|short,tirex,1.13
+Q|short,patchtst-fm,-0.24999999999999997
+Q|short,flowstate,-0.22
+Q|short,timesfm-2.5,0.12999999999999998
+W|short,toto-2.0-2.5b,0.43666666666666665
+W|short,chronos-2,-1.7333333333333334
+W|short,tirex,1.6666666666666665
+W|short,patchtst-fm,-1.5333333333333332
+W|short,flowstate,0.4466666666666667
+W|short,timesfm-2.5,0.7166666666666667

manifest.json

@@ -0,0 +1,1652 @@
+{
+  "format": "caststar-gift-eval-inference-v1",
+  "models": [
+    "toto-2.0-2.5b",
+    "chronos-2",
+    "tirex",
+    "patchtst-fm",
+    "flowstate",
+    "timesfm-2.5"
+  ],
+  "quantile_levels": [
+    0.1,
+    0.2,
+    0.3,
+    0.4,
+    0.5,
+    0.6,
+    0.7,
+    0.8,
+    0.9
+  ],
+  "feature_columns": [
+    "hurst",
+    "series_length",
+    "unitroot_pp",
+    "unitroot_kpss",
+    "hw_alpha",
+    "hw_beta",
+    "hw_gamma",
+    "stability",
+    "nperiods",
+    "seasonal_period",
+    "trend",
+    "spike",
+    "linearity",
+    "curvature",
+    "e_acf1",
+    "e_acf10",
+    "seasonal_strength",
+    "peak",
+    "trough",
+    "x_pacf5",
+    "diff1x_pacf5",
+    "diff2x_pacf5",
+    "seas_pacf",
+    "nonlinearity",
+    "lumpiness",
+    "alpha",
+    "beta",
+    "arch_acf",
+    "garch_acf",
+    "arch_r2",
+    "garch_r2",
+    "flat_spots",
+    "entropy",
+    "crossing_points",
+    "arch_lm",
+    "x_acf1",
+    "x_acf10",
+    "diff1_acf1",
+    "diff1_acf10",
+    "diff2_acf1",
+    "diff2_acf10",
+    "seas_acf1",
+    "seasonality",
+    "prediction_length",
+    "num_variates",
+    "freq",
+    "domain"
+  ],
+  "categories": {
+    "domain": [
+      "Econ/Fin",
+      "Energy",
+      "Healthcare",
+      "Nature",
+      "Sales",
+      "Transport",
+      "Web/CloudOps"
+    ],
+    "freq": [
+      "10S",
+      "10T",
+      "15T",
+      "5T",
+      "A-DEC",
+      "D",
+      "H",
+      "M",
+      "Q-DEC",
+      "W-FRI",
+      "W-SUN",
+      "W-THU",
+      "W-TUE",
+      "W-WED"
+    ]
+  },
+  "configs": [
+    {
+      "dirname": "bitbrains_fast_storage_5T_long",
+      "dataset_name": "bitbrains_fast_storage/5T",
+      "dataset_config": "bitbrains_fast_storage/5T/long",
+      "bucket": "5T|long",
+      "freq": "5T",
+      "term": "long",
+      "prediction_length": 720,
+      "seasonality": 288,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 2,
+      "test_rows": 5000,
+      "normalizer_mase": 1.1366422318753115,
+      "normalizer_crps": 1.1773077032683998
+    },
+    {
+      "dirname": "bitbrains_fast_storage_5T_medium",
+      "dataset_name": "bitbrains_fast_storage/5T",
+      "dataset_config": "bitbrains_fast_storage/5T/medium",
+      "bucket": "5T|medium",
+      "freq": "5T",
+      "term": "medium",
+      "prediction_length": 480,
+      "seasonality": 288,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 2,
+      "test_rows": 5000,
+      "normalizer_mase": 1.2202713595882653,
+      "normalizer_crps": 1.1979128978088514
+    },
+    {
+      "dirname": "bitbrains_fast_storage_5T_short",
+      "dataset_name": "bitbrains_fast_storage/5T",
+      "dataset_config": "bitbrains_fast_storage/5T/short",
+      "bucket": "5T|short",
+      "freq": "5T",
+      "term": "short",
+      "prediction_length": 48,
+      "seasonality": 288,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 18,
+      "test_rows": 45000,
+      "normalizer_mase": 1.1360241737534567,
+      "normalizer_crps": 1.210386425305063
+    },
+    {
+      "dirname": "bitbrains_fast_storage_H_short",
+      "dataset_name": "bitbrains_fast_storage/H",
+      "dataset_config": "bitbrains_fast_storage/H/short",
+      "bucket": "H|short",
+      "freq": "H",
+      "term": "short",
+      "prediction_length": 48,
+      "seasonality": 24,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 2,
+      "test_rows": 5000,
+      "normalizer_mase": 1.2985124569638047,
+      "normalizer_crps": 1.0222395520114795
+    },
+    {
+      "dirname": "bitbrains_rnd_5T_long",
+      "dataset_name": "bitbrains_rnd/5T",
+      "dataset_config": "bitbrains_rnd/5T/long",
+      "bucket": "5T|long",
+      "freq": "5T",
+      "term": "long",
+      "prediction_length": 720,
+      "seasonality": 288,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 2,
+      "test_rows": 2000,
+      "normalizer_mase": 3.501257904408163,
+      "normalizer_crps": 1.175220506770106
+    },
+    {
+      "dirname": "bitbrains_rnd_5T_medium",
+      "dataset_name": "bitbrains_rnd/5T",
+      "dataset_config": "bitbrains_rnd/5T/medium",
+      "bucket": "5T|medium",
+      "freq": "5T",
+      "term": "medium",
+      "prediction_length": 480,
+      "seasonality": 288,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 2,
+      "test_rows": 2000,
+      "normalizer_mase": 4.542392314855541,
+      "normalizer_crps": 1.169291300089368
+    },
+    {
+      "dirname": "bitbrains_rnd_5T_short",
+      "dataset_name": "bitbrains_rnd/5T",
+      "dataset_config": "bitbrains_rnd/5T/short",
+      "bucket": "5T|short",
+      "freq": "5T",
+      "term": "short",
+      "prediction_length": 48,
+      "seasonality": 288,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 18,
+      "test_rows": 18000,
+      "normalizer_mase": 1.9708126376716957,
+      "normalizer_crps": 1.1015904440824371
+    },
+    {
+      "dirname": "bitbrains_rnd_H_short",
+      "dataset_name": "bitbrains_rnd/H",
+      "dataset_config": "bitbrains_rnd/H/short",
+      "bucket": "H|short",
+      "freq": "H",
+      "term": "short",
+      "prediction_length": 48,
+      "seasonality": 24,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 2,
+      "test_rows": 2000,
+      "normalizer_mase": 6.0373618687533925,
+      "normalizer_crps": 1.2432170953390527
+    },
+    {
+      "dirname": "bizitobs_application_10S_long",
+      "dataset_name": "bizitobs_application",
+      "dataset_config": "bizitobs_application/10S/long",
+      "bucket": "10S|long",
+      "freq": "10S",
+      "term": "long",
+      "prediction_length": 900,
+      "seasonality": 360,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 1,
+      "test_rows": 2,
+      "normalizer_mase": 3.2063450561622804,
+      "normalizer_crps": 0.04573090228155046
+    },
+    {
+      "dirname": "bizitobs_application_10S_medium",
+      "dataset_name": "bizitobs_application",
+      "dataset_config": "bizitobs_application/10S/medium",
+      "bucket": "10S|medium",
+      "freq": "10S",
+      "term": "medium",
+      "prediction_length": 600,
+      "seasonality": 360,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 2,
+      "test_rows": 4,
+      "normalizer_mase": 2.6914183385868085,
+      "normalizer_crps": 0.04268912394422428
+    },
+    {
+      "dirname": "bizitobs_application_10S_short",
+      "dataset_name": "bizitobs_application",
+      "dataset_config": "bizitobs_application/10S/short",
+      "bucket": "10S|short",
+      "freq": "10S",
+      "term": "short",
+      "prediction_length": 60,
+      "seasonality": 360,
+      "num_variates": 2,
+      "domain": "Web/CloudOps",
+      "windows": 15,
+      "test_rows": 30,
+      "normalizer_mase": 2.2423302386755415,
+      "normalizer_crps": 0.03483868247005456
+    },
+    {
+      "dirname": "bizitobs_l2c_5T_long",
+      "dataset_name": "bizitobs_l2c/5T",
+      "dataset_config": "bizitobs_l2c/5T/long",
+      "bucket": "5T|long",
+      "freq": "5T",
+      "term": "long",
+      "prediction_length": 720,
+      "seasonality": 288,
+      "num_variates": 7,
+      "domain": "Web/CloudOps",
+      "windows": 5,
+      "test_rows": 35,
+      "normalizer_mase": 1.4542830442631411,
+      "normalizer_crps": 0.6484921267253538
+    },
+    {
+      "dirname": "bizitobs_l2c_5T_medium",
+      "dataset_name": "bizitobs_l2c/5T",
+      "dataset_config": "bizitobs_l2c/5T/medium",
+      "bucket": "5T|medium",
+      "freq": "5T",
+      "term": "medium",
+      "prediction_length": 480,
+      "seasonality": 288,
+      "num_variates": 7,
+      "domain": "Web/CloudOps",
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