Applying LSTM to time series predictable through time-window approaches

Felix A. Gers, Douglas Eck, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

143 Scopus citations


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 languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages8
ISBN (Print)3540424865
StatePublished - Jan 1 2001
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14


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