> ## Documentation Index
> Fetch the complete documentation index at: https://wwwpoc.ibm.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Forecast Energy Demand

> Use the Granite Time Series model to forecast energy demand.

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 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. This notebook demonstrates how to apply Granite Time
Series TTMs to forecast energy demand.

You will need a Hugging Face token to run this recipe in Colab. Instructions for obtaining this credential can be found [here](/granite/docs/use-cases/getting-credentials).

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  <Card title="Get started" icon="github" href="https://github.com/ibm-granite-community/granite-timeseries-cookbook/blob/main/recipes/Time_Series/Time_Series_Getting_Started.ipynb">
    Explore sample code in a GitHub repo
  </Card>

  <Card title="Try it out" icon="https://mintcdn.com/ibmgranite/m3dncz2KrKeb3pcV/granite/docs/images/icons8-google-colab.svg?fit=max&auto=format&n=m3dncz2KrKeb3pcV&q=85&s=fb39ef667c012d0fcef53599b6c5c0fd" href="https://colab.research.google.com/github/ibm-granite-community/granite-timeseries-cookbook/blob/main/recipes/Time_Series/Time_Series_Getting_Started.ipynb" width="144" height="144" data-path="granite/docs/images/icons8-google-colab.svg">
    Execute sample code in Colab
  </Card>
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