granite-timeseries-ttm-v1
Maintainer: ibm-granite
109
❗
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The granite-timeseries-ttm-v1 model is a compact pre-trained model for Multivariate Time-Series Forecasting, open-sourced by IBM Research. With less than 1 Million parameters, it introduces the notion of the first-ever tiny pre-trained models for Time-Series Forecasting. The TinyTimeMixer (TTM) model outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. The current open-source version supports point forecasting use-cases ranging from minutely to hourly resolutions.
Model inputs and outputs
Inputs
- Multivariate time-series data: The model takes in multivariate time-series data as input, where the number of time-points (context length) can range from 512 to 1024.
Outputs
- Future time-series forecasts: Given the input time-series data, the model generates forecasts for the next 96 time-points (forecast length) in the future.
Capabilities
The granite-timeseries-ttm-v1 model outperforms several popular pre-trained SOTA approaches in both zero-shot and few-shot forecasting. For example, it surpasses the few-shot results of models like PatchTST, PatchTSMixer, and TimesNet in its zero-shot forecasts. The model also demonstrates the ability to provide state-of-the-art zero-shot forecasts and can be quickly fine-tuned with just 5% of the target data to achieve competitive results.
What can I use it for?
You can use the granite-timeseries-ttm-v1 model for a variety of time-series forecasting applications, such as electricity demand forecasting, stock price prediction, weather forecasting, and more. The model's compact size and fast inference makes it suitable for deployment on resource-constrained environments, like edge devices or laptops. Additionally, the provided notebooks and scripts can help you get started with using the model for your own time-series forecasting tasks.
Things to try
One interesting aspect of the granite-timeseries-ttm-v1 model is its ability to provide state-of-the-art zero-shot forecasts. This means you can apply the pre-trained model directly to your target data without any fine-tuning and still get accurate predictions. You can also try fine-tuning the model with just a small portion of your target data (e.g., 5%) to further improve the forecasting accuracy. The provided notebooks showcase these capabilities and can serve as a starting point for your experiments.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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