Time Series Forecasting in the Presence of Concept Drift: A PSO-based Approach

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.