Multi-task learning of time series and its application to the travel demand

TL;DR

This paper introduces a multi-task learning framework using support vector regression for joint modeling and prediction of multiple related time series in transportation, improving travel demand forecasts.

Contribution

It extends regularization-based multi-task learning to time series and incorporates dynamic time warping for identifying related series and aligning them via latent representations.

Findings

Multi-task learning improves travel demand prediction accuracy.

Dynamic time warping effectively identifies related time series.

Aligning series via latent representations enhances model performance.

Abstract

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.