Oliveira, Gustavo H. F. M.
Cavalcante, Rodolfo C.
Cabral, George G.
Minku, Leandro L.
Oliveira, Adriano L. I.
Time Series Forecasting in the Presence of Concept Drift: A PSO-based Approach
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.
IR content
2018-05-21
https://leicester.figshare.com/articles/Time_Series_Forecasting_in_the_Presence_of_Concept_Drift_A_PSO-based_Approach/10196681