This repository contains the code & results of a multi-step time series forecasting exercise I performed with deep learning models, on a large dataset of hourly energy consumption values.

I used PyTorch Lightning to implement a stateful LSTM model, and an inverted Transformer model, with some modifications inspired by multiple other time series forecasting architectures. Most notably, I implemented a simple linear extrapolation method in the Transformer model, as a simple way to initialize target variable values for the decoder target sequence.

See the Markdown report for an explanation of the models & results, along with sources & acknowledgements.

I also used this dataset and the GPyTorch package to try out Gaussian Process Regression with various training strategies. See the notebook for details.

The dataset is available on Kaggle.