This page contains the explanation of our Long Short-Term Memory Multi-Seasonal Net (LSTM-MSNet) forecasting framework, which can be used to forecast a sets of time series with multiple seasonal patterns.

In the description, we first provide a breif introduction to our methdology, and then explain the steps to be followed to execute our code and use our framework for your research work.

The above figure gives an overview of the proposed LSTM-MSNet training paradigms. In the DS approach, deseasonalised time series are used to train the LSTM-MSNet. Here, a reseasonalisation phase is required as the target MW patches are seasonally adjusted. Whereas in the SE approach, the seasonal values extracted from the deseasonalisation phase are employed as exogenous variables, along with the original time series to train the LSTM-MSNet. Here a reseasonalisation phase is not required as the target MW patches contain the original distribution of the time series. A more detailed explaination of these training paradigms can be found in our manuscript.

We used DS and SE naming conventions in our code repository to distinguish these training paradigms. Please note that this repo contains seperate preprocessing files for each of these training paradigms.

As illustrated in the above figure, the LSTM-MSNet framework consists of three main phases: i) pre-processing phase: using state-of-the-art multi-seasonal decomposition techniques, i.e., MSTL, Prophet, Tbats to extract the seasonal components. Additonally, for the SE approach fourier terms have used to denote the seasonal trajectories (in order to supplement the subsequent LSTM training phase) ii) training phase: LSTM-MSNet framework training and iii) post-processing phase: retransform the forecasts into original scale.

Set the PYTHONPATH env variable of the system. Append absolute paths of both the project root directory and the directory of the external_packages/cocob_optimizer into the PYTHONPATH

For R scripts (under src/LSTM-Preprocessing-Scripts), make sure to set the working directory to the project root folder. As an example, solar_train.txt file is hardcoded in the scripts. The current source code supports for comma seperated data input, however this can be easily adjustable for other delimiters.