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Energy Demand Forecasting - Basic Inference

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.

Prerequisites

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

Lab

Energy Demand Forecasting - Basic Inference notebook Energy Demand Forecasting - Basic Inference notebook

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

jupyter notebook notebooks/Time_Series_Getting_Started.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 - Basic Inference notebook. Refer to the IBM Granite Community for the official notebooks.