Collective Prediction of Individual Mobility Traces with Exponential Weights

TL;DR

This paper introduces a sequential learning algorithm combining exponential weights with a large ensemble of experts for short-term human mobility prediction, significantly outperforming individual models using mobile phone location data.

Contribution

The paper presents a novel ensemble-based prediction algorithm that leverages exponential weights to improve short-term human mobility forecasts from large datasets.

Findings

Prediction accuracy exceeds individual Markov models.

Algorithm effectively utilizes large, redundant mobility datasets.

Applicable to various sequence prediction tasks.

Abstract

We present and test a sequential learning algorithm for the short-term prediction of human mobility. This novel approach pairs the Exponential Weights forecaster with a very large ensemble of experts. The experts are individual sequence prediction algorithms constructed from the mobility traces of 10 million roaming mobile phone users in a European country. Average prediction accuracy is significantly higher than that of individual sequence prediction algorithms, namely constant order Markov models derived from the user's own data, that have been shown to achieve high accuracy in previous studies of human mobility prediction. The algorithm uses only time stamped location data, and accuracy depends on the completeness of the expert ensemble, which should contain redundant records of typical mobility patterns. The proposed algorithm is applicable to the prediction of any sufficiently large dataset of sequences.