Time series forecasting in the presence of concept drift: a PSO-based approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • G.H.F.M. OLIVEIRA
  • R.C. CAVALCANTE
  • G.G. CABRAL
  • Leandro Minku
  • A.L.I.. OLIVEIRA

Colleges, School and Institutes

Abstract

Time series forecasting is a problem with many applications. However, in many domains, such as stock market, the underlying generating process of the time series observations may change, making forecasting models obsolete. This problem is known as Concept Drift. Approaches for time series forecasting should be able to detect and react to concept drift in a timely manner, so that the forecasting model can be updated as soon as possible. Despite the fact that the concept drift problem is well investigated in the literature, little effort has been made to solve this problem for time series forecasting so far. This work proposes two novel methods for dealing with the time series forecasting problem in the presence of concept drift. The proposed methods benefit from the Particle Swarm Optimization (PSO) technique to detect and react to concept drifts in the time series data stream. It is expected that the use of collective intelligence of PSO makes the proposed method more robust to false positive drift detections while maintaining a low error rate on the forecasting task. Experiments show that the methods achieved competitive results in comparison to state-of-the-art methods.

Details

Original languageEnglish
Title of host publication2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)
Publication statusPublished - 7 Jun 2018
EventIEEE ICTAI 2017 : 29th IEEE International Conference on Tools with Artificial Intelligence - Boston, United States
Duration: 6 Nov 20178 Nov 2017

Publication series

NameInternational Conference on Tools with Artificial Intelligence (ICTAI)
PublisherIEEE
VolumeNov-2017
ISSN (Electronic)2375-0197

Conference

ConferenceIEEE ICTAI 2017
Abbreviated titleICTAI 2017
CountryUnited States
CityBoston
Period6/11/178/11/17

Keywords

  • Time series, Forecasting, Concept drift, Particle swarm optimization, Data streams