---
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.