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Energy Demand Forecasting with Granite Timeseries (TTM)

Forecasting in time series analysis allows data scientists to identify patterns by using machine learning and then generate forecasts about the future. TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting.

The goal of this lab is to show how you can predict future trends on historical data using the IBM Granite Time Series models.

Pre-requisite

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

Lab

Energy Demand Forecasting with Granite Timeseries (TTM) notebook Energy Demand Forecasting with Granite Timeseries (TTM) 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 with Granite Timeseries (TTM) notebook. Refer to the IBM Granite Community for the official notebooks.