Applying {LSTM} to Time Series Predictable Through Time-Window Approaches

F. A. Gers
J. Schmidhuber
Artificial Neural Networks -- ICANN 2001 (Proceedings), Springer, Berlin, pp. 669-676


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 {\em not} carry over to certain simpler time series tasks solvable by time window approaches: the Mackey-Glass series and the Santa Fe FIR laser emission series (Set A). This suggests t use LSTM only when simpler traditional approaches fail.

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