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Energy Demand Forecasting - Few-shot Fine-tuning

TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. With less than 1 Million parameters, TTM introduces the notion of the first-ever "tiny" pre-trained models for Time-Series Forecasting. TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting and can easily be fine-tuned for multi-variate forecasts.

In this lab, we move beyond zero-shot prediction to few-shot fine-tuning and prediction. We use a real-world dataset containing energy demand data from Spain.

Prerequisites

This lab is a Jupyter notebook. Please follow the instructions in pre-work to run the lab.

Lab

Energy Demand Forecasting - Few-shot Fine-tuning notebook Energy Demand Forecasting - Few-shot Fine-tuning notebook

To run the notebook from your command line in Jupyter using the active virtual environment from the pre-work, run:

jupyter notebook notebooks/Few-shot_Finetuning_and_Evaluation.ipynb

The path of the notebook file above is relative to the granite-workshop folder from the git clone in the pre-work.

Credits

This notebook is a modified version of the IBM Granite Community Energy Demand Forecasting - Few-shot Fine-tuning notebook. Refer to the IBM Granite Community for the official notebooks.