Predictive modeling with notion of time

Mark Hoogendoorn; Burkhardt Funk

doi:10.1007/978-3-319-66308-1_8

Predictive modeling with notion of time

Mark Hoogendoorn^*, Burkhardt Funk

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-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 language	English
Title of host publication	Machine Learning for the Quantified Self
Subtitle of host publication	On the Art of Learning from Sensory Data
Publisher	Springer/Verlag
Chapter	8
Pages	167-202
Number of pages	36
ISBN (Electronic)	9783319663081
ISBN (Print)	9783319663074
DOIs	https://doi.org/10.1007/978-3-319-66308-1_8
Publication status	Published - 1 Jan 2018

Publication series

Name	Cognitive Systems Monographs
Volume	35
ISSN (Print)	1867-4925
ISSN (Electronic)	1867-4933

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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.",

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ER -

Predictive modeling with notion of time

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