DYNAMIC INTERACTION NETWORKS VERSUS LOCAL TREND MODELS FOR MULTIPLE TIME-SERIES PREDICTION
Harya Widiputra, Russel Pears, Nikola Kirilov Kasabov · Cybernetics & Systems · 2011
Time-series modeling and prediction have been very well researched by both the statistical and data mining communities. However, the multiple time-series problem of modeling and predicting simultaneous movements of a collection of time-sensitive variables that are related to each other has received much less attention. Strong relationships between variables suggest that trajectories of given variables involved in the relationships can be improved by including the nature and strength of these relationships in a prediction model. The key challenge is to capture the dynamics of the relationships to reflect changes that take place continuously over time. This research presents a global model to capture inclusive patterns of dynamic interactions between multiple time-series and a local trend model to extract localized profiles of relationships and recurring trends in multiple time-series. Our experimentation revealed that the global and local models specially developed for multiple time-series prediction outperformed methods such as multiple linear regression and the multilayer perceptron that were developed for predicting single time-series.