Predictive modeling with notion of time

Mark Hoogendoorn*, Burkhardt Funk

*Corresponding author for this work

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

Fingerprint Dive into the research topics of 'Predictive modeling with notion of time'. Together they form a unique fingerprint.

  • 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