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Recurrent Neural Networks for Time Series Forecasting
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Recurrent Neural Networks for Time Series Forecasting
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Time series forecasting is difficult. It is difficult even for recurrent neural networks with their inherent ability to learn sequentiality. This article presents a recurrent neural network based time series forecasting framework covering feature engineering, feature importances, point and interval predictions, and forecast evaluation. The description of the method is followed by an empirical study using both LSTM and GRU networks.
Forward citations
Cited by 1 Pith paper
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Channel-wise Retrieval for Multivariate Time Series Forecasting
CRAFT improves multivariate time series forecasting accuracy by performing independent channel-wise retrieval via time-domain sparse pruning followed by frequency-domain spectral ranking.
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