---
license: cc
---
## About

This repository provides model weights to run load forecasting models trained on ComStock datasets. The companion dataset repository is [this](https://huggingface.co/datasets/APPFL/Illinois_load_datasets). The model definitions are present in the `models` directory. The corresponding trained model weights are present in the `weights` directory. The corresponding model keyword arguments (as a function of a provided `lookback` and `lookahead`) can be imported from the file `model_kwargs.py`.

Note that `lookback` is denoted by `L` and `lookahead` by `T` in the weights directory. We provide weights for the following `(L,T)` pairs: `(512,4)`, `(512,48)`, and `(512,96)`, and for `HOM`ogenous and `HET`erogenous datasets.

## Data

When using the companion [dataset](https://huggingface.co/datasets/APPFL/Illinois_load_datasets), the following points must be noted (see the page for more information on configuring the data loaders):

- All models accept normalized inputs and produce normalized outputs, i.e. set `normalize = True` when generating the datasets.
- For Transformer, Autoformer, Informer, and TimesNet set `transformer = True`, while for LSTM, LSTNet, and PatchTST set `transformer = False`.

## Packages

Executing the code only requires `numpy` and `torch` (PyTorch) packages. You can either have them in your Python base installation, or use a `conda` environment.

## Example

In order to see how to use the model definitions and load the weights into them, see `example.py`.

## Credits

Some model definitions have been adapted from the code provided in the [TSLib Library](https://github.com/thuml/Time-Series-Library).