CastStar / README.md
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metadata
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

The original model repositories retain their respective licenses and terms.

Artifact Layout

CastStar-HF/
β”œβ”€β”€ README.md
β”œβ”€β”€ manifest.json
β”œβ”€β”€ calibration/
β”‚   └── bucket_class_biases.csv
β”œβ”€β”€ models/
β”‚   β”œβ”€β”€ pre/
β”‚   └── post/
└── test/
    β”œβ”€β”€ input_features/
    β”œβ”€β”€ 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.
  2. Download the Salesforce/GiftEval dataset.
  3. Open 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

{
  "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.