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Kernel Corrector LSTM
We empirically evaluate the forecasting accuracy and the training time of the new algorithm and compare it with cLSTM and LSTM.
On-the-fly Data Augmentation for Forecasting with Deep Learning
We validated the proposed approach using a state-of-the-art deep learning forecasting method and 8 benchmark datasets containing a total of 75797 time series.
Pastprop-RNN: improved predictions of the future by correcting the past
Forecasting accuracy is reliant on the quality of available past data.
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