license: cc
About
This repository provides model weights to run load forecasting models trained on ComStock datasets. The companion dataset repository is this. 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 HOMogenous and HETerogenous datasets.
Data
When using the companion dataset, 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 = Truewhen generating the datasets. - For Transformer, Autoformer, Informer, and TimesNet set
transformer = True, while for LSTM, LSTNet, and PatchTST settransformer = 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.