An approach to handle concept drift in financial time series based on Extreme Learning Machines and explicit Drift Detection

Financial markets are very important to the economical and social organization of modern society. Due to they importance, several researchers have investigated how to predict future market movements by using both statistical and soft computing methods based on historical time series data. However, as a typical data stream, financial time series frequently present concept drift, which is a change in the relationship between input data and the target variable over time. The concept drift phenomenon affects negatively the forecasting accuracy since the learned model becomes outdated after a change in the current concept. In this paper we investigate how to handle concept drift in financial time series prediction in order to improve the forecasting accuracy. Two explicit drift detector mechanisms, namely the Drift Detection Mechanism (DDM) and the Exponentially Weighted Moving Average for Concept Drift Detection Mechanism (ECDD), were investigated. The main contribution of this work is an approach that combines Online Sequential Extreme Learning Machines (OS-ELM) with explicit drift detection, in which the OS-ELM updates the decision model just in the presence of concept drift in data. Experimental results showed that the use of drift detection was able to speed up the prediction time of OS-ELM maintaining equivalent accuracy.