Long Short-Term Memory (LSTM) is able to solve many time series tasks unsolvable by feed-forward networks using fixed size time windows. Here we find that LSTM's superiority does not carry over to certain simpler time series prediction tasks solvable by time window approaches: the Mackey-Glass series and the Santa Fe FIR laser emission series (Set A). This suggests to use LSTM only when simpler traditional approaches fail.
CITATION STYLE
Gers, F. A., Eck, D., & Schmidhuber, J. (2001). Applying LSTM to time series predictable through time-window approaches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2130, pp. 669–676). Springer Verlag. https://doi.org/10.1007/3-540-44668-0_93
Mendeley helps you to discover research relevant for your work.