| --- |
| license: apache-2.0 |
| pipeline_tag: time-series-forecasting |
| library_name: xgboost |
| base_model: |
| - amazon/chronos-2 |
| - google/timesfm-2.5-200m-pytorch |
| - ibm-research/flowstate |
| - NX-AI/TiRex-1.1-gifteval |
| - ibm-research/patchtst-fm-r1 |
| - Datadog/Toto-2.0-2.5B |
| tags: |
| - time-series-forecasting |
| - foundation-models |
| - time-series |
| - forecasting |
| - ensemble |
| - agentic |
| - gift-eval |
| - xgboost |
| --- |
| |
| # CastStar |
|
|
| 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 |
|
|
| | 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) | |
|
|
| The original model repositories retain their respective licenses and terms. |
|
|
| ## Artifact Layout |
|
|
| ```text |
| CastStar-HF/ |
| ├── README.md |
| ├── manifest.json |
| ├── calibration/ |
| │ └── bucket_class_biases.csv |
| ├── models/ |
| │ ├── pre/ |
| │ └── post/ |
| └── test/ |
| ├── input_features/ |
| ├── predictions/ |
| └── predicted_features/ |
| ``` |
|
|
| The bundle contains only the frozen files required by the reproduction |
| notebook. It does not contain training data or training code. |
|
|
| ## Reproduction |
|
|
| 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. |
|
|
| ## GIFT-Eval Submission |
|
|
| ```json |
| { |
| "model": "CastStar", |
| "model_type": "agentic", |
| "model_dtype": "float32", |
| "model_link": "https://huggingface.co/USTC-AGI/CastStar", |
| "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/caststar.ipynb", |
| "org": "USTC-AGI", |
| "testdata_leakage": "No", |
| "replication_code_available": "Yes" |
| } |
| ``` |
|
|
| ## Intended Use |
|
|
| These assets are intended for reproducing and inspecting the CastStar |
| GIFT-Eval benchmark submission. |
|
|
