Predictive Modeling without Notion of Time

  • Mark HoogendoornEmail author
  • Burkhardt Funk
Part of the Cognitive Systems Monographs book series (COSMOS, volume 35)


Supervised learning approaches that do not explicitly take the time component into account are briefly discussed in this chapter. The approaches explained include feedforward neural networks, support vector machines, k-nearest neighbor, decision trees, naïve bayes and ensembles. Guidelines are provided on how to apply these algorithms to quantified self data, including the learning setup (e.g. learning for single users or across multiple users) and other practical considerations such as feature selection and regularization. Data stream mining approaches for predictive modeling are also briefly discussed.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.Institut für WirtschaftsinformatikLeuphana Universität LüneburgLüneburgGermany

Personalised recommendations