ForecastAgent 1.0

ForecastAgent 1.0 is a professional, production-ready Python SDK and Serving API for Zero-Shot Time Series Forecasting, powered by the TiRex-2 xLSTM-based time series foundation model.

With ForecastAgent 1.0, you can run accurate, probabilistic time series forecasting out of the box with zero training, or deploy a self-hosted SaaS API for scalable production workloads.

Key Features

• Zero-Shot Generalization: Perform high-quality forecasting on new time series datasets without fine-tuning.
• Probabilistic Quantiles: Predicts 9 distinct quantiles (from 10th to 90th percentile) to model uncertainty.
• Covariate Support: Supports both past (historical) and future (known ahead of time) covariates.
• Windows & Linux Native: Includes automated monkeypatches to support execution on Windows hosts bypassing Triton and MSVC compiler dependencies.
• FastAPI Serve: Built-in CLI command to spin up a high-performance serving backend.

Installation

From PyPI

pip install forecast-agent-sdk

From Source

Clone this repository and install the package in editable mode:

git clone https://github.com/shinydatatech/ForecastAgent.git
cd ForecastAgent
pip install -e .

Dependencies

The package requires:

• torch>=2.0.0
• numpy
• xlstm>=2.0.0
• fastapi, uvicorn, pydantic
• huggingface_hub

Quickstart

1. Python SDK Usage

Load the model (either from Hugging Face or a local directory) and run zero-shot forecasting:

from forecastagent import ForecastAgent

# Load the model (automatically downloads weights from Hugging Face if not cached)
agent = ForecastAgent.from_pretrained("shinydatatech/forecastagent-v1.0")

# Input target history (e.g., hourly electricity consumption)
history = [10.2, 11.5, 12.1, 11.8, 13.0, 14.5, 15.2, 14.8, 13.9, 13.1]

# Predict 3 steps forward
results = agent.predict(
    target=history,
    prediction_length=3,
    freq="h"
)

print("Median Forecast (50th percentile):", results["median"])
print("Lower Bound (10th percentile):", results["lower"])
print("Upper Bound (90th percentile):", results["upper"])
print("All 9 Quantiles:", results["quantiles"])

2. Launch Serving API Server

You can launch the FastAPI server via the command-line interface:

# Launch server locally on port 8000
forecastagent-api --model-path shinydatatech/forecastagent-v1.0 --port 8000

API Endpoints

• GET /: Health check and model metadata.
• POST /v1/predict: Forecast endpoint.

  {
    "instances": [
      {
        "target": [10.2, 11.5, 12.1, 11.8, 13.0],
        "start": "2026-07-01T00:00:00",
        "freq": "h"
      }
    ],
    "prediction_length": 3
  }
  

Publishing Model & Package

For detailed step-by-step instructions on:

1. Publishing merged model checkpoints to Hugging Face
2. Building distribution packages (.whl / .tar.gz)
3. Setting up automated release pipelines via GitHub Actions

Please refer to the Implementation & Publishing Guide.

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.