Applying LSTM to time series predictable through time-window approaches

Felix A. Gers; Douglas Eck; Jürgen Schmidhuber

doi:10.1007/3-540-44668-0_93

Applying LSTM to time series predictable through time-window approaches

Felix A. Gers, Douglas Eck, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

157 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer [email protected]
Pages	669-676
Number of pages	8
ISBN (Print)	3540424865
DOIs	https://doi.org/10.1007/3-540-44668-0_93
State	Published - Jan 1 2001
Externally published	Yes

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10.1007/3-540-44668-0_93

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title = "Applying LSTM to time series predictable through time-window approaches",

abstract = "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.",

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Applying LSTM to time series predictable through time-window approaches. / Gers, Felix A.; Eck, Douglas; Schmidhuber, Jürgen.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer [email protected], 2001. p. 669-676.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Applying LSTM to time series predictable through time-window approaches

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