Multi-task learning of time series and its application to the travel demand
This work addresses travel demand forecasting for public transport systems, representing an incremental improvement with domain-specific application.
The authors tackled the problem of predicting travel demand by modeling multiple temporal events using a multi-task learning framework, achieving improved predictions through joint learning of related time series.
We address the problem of modeling and prediction of a set of temporal events in the context of intelligent transportation systems. To leverage the information shared by different events, we propose a multi-task learning framework. We develop a support vector regression model for joint learning of mutually dependent time series. It is the regularization-based multi-task learning previously developed for the classification case and extended to time series. We discuss the relatedness of observed time series and first deploy the dynamic time warping distance measure to identify groups of similar series. Then we take into account both time and scale warping and propose to align multiple time series by inferring their common latent representation. We test the proposed models on the problem of travel demand prediction in Nancy (France) public transport system and analyze the benefits of multi-task learning.