Granite Timeseries Patchtst

🚀 Transformers.js for Time Series Forecasting

Transformers.js is a JavaScript library that enables you to use pre - trained models for various tasks. This project adapts the ibm - granite/granite - timeseries - patchtst model with ONNX weights to be compatible with Transformers.js, specifically for time - series forecasting.

🚀 Quick Start

📦 Installation

If you haven't already, you can install the Transformers.js JavaScript library from NPM using the following command:

npm i @huggingface/transformers

💻 Usage Examples

Basic Usage

The following example demonstrates time series forecasting using the onnx - community/granite - timeseries - patchtst model:

import { PatchTSTForPrediction, Tensor } from "@huggingface/transformers";

const model_id = "onnx-community/granite-timeseries-patchtst";
const model = await PatchTSTForPrediction.from_pretrained(model_id, { dtype: "fp32" });

const dims = [64, 512, 7];
const prod = dims.reduce((a, b) => a * b, 1);
const past_values = new Tensor('float32',
    Float32Array.from({ length: prod }, (_, i) => i / prod),
    dims,
);
const { prediction_outputs } = await model({ past_values });
console.log(prediction_outputs);

📚 Documentation

The original model can be found at https://huggingface.co/ibm - granite/granite - timeseries - patchtst.

⚠️ Important Note

Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web - ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).