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Erschienen in:
2001 | OriginalPaper | Buchkapitel
Applying LSTM to Time Series Predictable through Time-Window Approaches
verfasst von : Felix A. Gers, Douglas Eck, Jürgen Schmidhuber
Erschienen in: Artificial Neural Networks — ICANN 2001
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.