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Evaluating the effectiveness of predicting covariates in LSTM Networks for Time Series Forecasting
April 30, 2024, 4:42 a.m. | Gareth Davies
cs.LG updates on arXiv.org arxiv.org
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
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