Analysis of Machine Learning Approaches’ Performance in Prediction Problems with Human Activity Patterns

Ricardo Torres-López, David Casillas-Pérez, Jorge Pérez-Aracil, Laura Cornejo-Bueno, Enrique Alexandre and Sancho Salcedo-Sanz
Additional contact information
Ricardo Torres-López: Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
David Casillas-Pérez: Department of Signal Processing and Communications, Universidad Rey Juan Carlos, 28942 Fuenlabrada, Spain
Jorge Pérez-Aracil: Department of Computer Systems Engineering, Universidad Politécnica de Madrid, 28038 Madrid, Spain
Laura Cornejo-Bueno: Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
Enrique Alexandre: Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
Sancho Salcedo-Sanz: Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Spain

Mathematics, 2022, vol. 10, issue 13, 1-18

Abstract: Prediction problems in timed datasets related to human activities are especially difficult to solve, because of the specific characteristics and the scarce number of predictive (input) variables available to tackle these problems. In this paper, we try to find out whether Machine Learning (ML) approaches can be successfully applied to these problems. We deal with timed datasets with human activity patterns, in which the input variables are exclusively related to the day or type of day when the prediction is carried out and, usually, to the meteorology of those days. These problems with a marked human activity pattern frequently appear in mobility and traffic-related problems, delivery prediction (packets, food), and many other activities, usually in cities. We evaluate the performance in these problems of different ML methods such as artificial neural networks (multi-layer perceptrons, extreme learning machines) and support vector regression algorithms, together with an Analogue-type (KNN) approach, which serves as a baseline algorithm and provides information about when it is expected that ML approaches will fail, by looking for similar situations in the past. The considered ML algorithms are evaluated in four real prediction problems with human activity patterns, such as school absences, bike-sharing demand, parking occupation, and packets delivered in a post office. The results obtained show the good performance of the ML algorithms, revealing that they can deal with scarce information in all the problems considered. The results obtained have also revealed the importance of including meteorology as the input variables, showing that meteorology is frequently behind demand peaks or valleys in this kind of problem. Finally, we show that having a number of similar situations in the past (training set) prevents ML algorithms from making important mistakes in the prediction obtained.

Keywords: human activity patterns; machine learning approaches; performance evaluation; regression algorithms; timed data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/13/2187/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/13/2187/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:13:p:2187-:d:845792

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().