Predictive modeling with notion of time

Mark Hoogendoorn, Burkhardt Funk

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

This chapter focuses on supervised learning approaches that do take time into account explicitly. Times series approaches are explained as well as recurrent neural networks (including echo state networks). In addition, parameter optimization techniques that can be used to fine-tune more knowledge driven predictive temporal models (dynamical systems models) are discussed.

Original languageEnglish
Title of host publicationMachine Learning for the Quantified Self
Subtitle of host publicationOn the Art of Learning from Sensory Data
PublisherSpringer/Verlag
Chapter8
Pages167-202
Number of pages36
ISBN (Electronic)9783319663081
ISBN (Print)9783319663074
DOIs
Publication statusPublished - 1 Jan 2018

Publication series

NameCognitive Systems Monographs
Volume35
ISSN (Print)1867-4925
ISSN (Electronic)1867-4933

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Recurrent neural networks
Supervised learning
Time series
Dynamical systems

Cite this

Hoogendoorn, M., & Funk, B. (2018). Predictive modeling with notion of time. In Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data (pp. 167-202). (Cognitive Systems Monographs; Vol. 35). Springer/Verlag. https://doi.org/10.1007/978-3-319-66308-1_8
Hoogendoorn, Mark ; Funk, Burkhardt. / Predictive modeling with notion of time. Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag, 2018. pp. 167-202 (Cognitive Systems Monographs).
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Hoogendoorn, M & Funk, B 2018, Predictive modeling with notion of time. in Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Cognitive Systems Monographs, vol. 35, Springer/Verlag, pp. 167-202. https://doi.org/10.1007/978-3-319-66308-1_8

Predictive modeling with notion of time. / Hoogendoorn, Mark; Funk, Burkhardt.

Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag, 2018. p. 167-202 (Cognitive Systems Monographs; Vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

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Hoogendoorn M, Funk B. Predictive modeling with notion of time. In Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag. 2018. p. 167-202. (Cognitive Systems Monographs). https://doi.org/10.1007/978-3-319-66308-1_8