A case study comparing machine learning with statistical methods for time series forecasting: size matters

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Cerqueira, V (Author) Other; Torgo, L (Author) FCUP; Carlos Soares (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: JOURNAL OF INTELLIGENT INFORMATION SYSTEMS

Vol. 59

Pages: 415-433

ISSN: 0925-9902

Indexing

ISI Web of Knowledge - 20 Citations

Scopus - 26 Citations

Other information

Authenticus ID: P-00S-6VH

DOI: 10.1007/s10844-022-00713-9

Abstract (EN): Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent work, evidence was shown that these approaches systematically present a lower predictive performance relative to simple statistical methods. In this work, we counter these results. We show that these are only valid under an extremely low sample size. Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows. The R code to reproduce all of our experiments is available at https://github.com/vcerqueira/MLforForecasting.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19