Towards the use of vector based GP to predict physiological time series

Irene Azzali, Leonardo Vanneschi, Illya Bakurov, Sara Silva, Marco Ivaldi, Mario Giacobini

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
1 Downloads (Pure)


Prediction of physiological time series is frequently approached by means of machine learning (ML) algorithms. However, most ML techniques are not able to directly manage time series, thus they do not exploit all the useful information such as patterns, peaks and regularities provided by the time dimension. Besides advanced ML methods such as recurrent neural network that preserve the ordered nature of time series, a recently developed approach of genetic programming, VE-GP, looks promising on the problem in analysis. VE-GP allows time series as terminals in the form of a vector, including new strategies to exploit this representation. In this paper we compare different ML techniques on the real problem of predicting ventilation flow from physiological variables with the aim of highlighting the potential of VE-GP. Experimental results show the advantage of applying this technique in the problem and we ascribe the good performances to the ability of properly catching meaningful information from time series.

Original languageEnglish
Article number106097
JournalApplied Soft Computing Journal
Issue numberApril
Publication statusPublished - 1 Apr 2020


  • Genetic programming
  • Machine learning
  • Physiological data
  • Time series
  • Ventilation


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