Evaluating the effectiveness of predicting covariates in LSTM Networks for Time Series Forecasting

arXiv:2404.18553v1 Announce Type: new
Abstract: Autoregressive Recurrent Neural Networks are widely employed in time-series forecasting tasks, demonstrating effectiveness in univariate and certain multivariate scenarios. However, their inherent structure does not readily accommodate the integration of future, time-dependent covariates. A proposed solution, outlined by Salinas et al 2019, suggests forecasting both covariates and the target variable in a multivariate framework. In this study, we conducted comprehensive tests on publicly available time-series datasets, artificially introducing highly correlated covariates to future time-step values. …

abstract arxiv autoregressive cs.ai cs.lg forecasting future however integration lstm multivariate networks neural networks recurrent neural networks series solution tasks time series time series forecasting type