tsLSTMx:Predict Time Series Using LSTM Model Including Exogenous
Variable to Denote Zero Values
It is a versatile tool for predicting time series data using Long Short-Term Memory (LSTM) models. It is specifically
designed to handle time series with an exogenous variable,
allowing users to denote whether data was available for a
particular period or not. The package encompasses various
functionalities, including hyperparameter tuning, custom loss
function support, model evaluation, and one-step-ahead
forecasting. With an emphasis on ease of use and flexibility,
it empowers users to explore, evaluate, and deploy LSTM models
for accurate time series predictions and forecasting in diverse
applications. More details can be found in Garai and Paul
(2023) <doi:10.1016/j.iswa.2023.200202>.
