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

TL;DR

This study evaluates how incorporating future covariates affects LSTM-based time series forecasting, revealing that joint training can sometimes improve performance but often leads to inferior results compared to univariate models, questioning the effectiveness of covariate integration.

Contribution

The paper introduces a simple, effective seasonal segmentation approach for LSTMs and provides a comprehensive empirical analysis of covariate inclusion in multivariate time series forecasting.

Findings

Joint training of covariates can improve model performance under certain conditions.

Multivariate predictions often perform worse than univariate models, even with informative covariates.

LSTM architectures may not benefit from covariate information in forecasting tasks.

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. Our evaluation aimed to assess the performance of an LSTM network when considering these covariates and compare it against a univariate baseline. As part of this study we introduce a novel approach using seasonal time segments in combination with an RNN architecture, which is both simple and extremely effective over long forecast horizons with comparable performance to many state of the art architectures. Our findings from the results of more than 120 models reveal that under certain conditions jointly training covariates with target variables can improve overall performance of the model, but often there exists a significant performance disparity between multivariate and univariate predictions. Surprisingly, even when provided with covariates informing the network about future target values, multivariate predictions exhibited inferior performance. In essence, compelling the network to predict multiple values can prove detrimental to model performance, even in the presence of informative covariates. These results suggest that LSTM architectures may not be suitable for forecasting tasks where predicting covariates would typically be expected to enhance model accuracy.