Multi-label Methods for Prediction with Sequential Data

TL;DR

This paper explores the connection between multi-label classification methods and sequential data prediction, developing new approaches and evaluating their effectiveness on real-world tasks like electricity demand and route prediction.

Contribution

It identifies links between multi-label and sequential data classification, and introduces two novel methods that perform competitively on real-world sequential prediction tasks.

Findings

Multi-label algorithms are applicable to sequential data.

Proposed methods outperform some established approaches.

Effective on electricity demand and route prediction datasets.

Abstract

The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of classification of sequential data. If labels indices are considered as time indices, the problems can often be seen as equivalent. In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data. From this study we draw upon the most suitable techniques from the area and develop two novel competitive approaches which can be applied to either kind of data. We carry out an empirical evaluation investigating performance on real-world sequential-prediction tasks: electricity demand, and route prediction. As well as showing that several popular multi-label algorithms are in fact easily applicable to sequencing tasks, our novel approaches, which benefit from a unified view of these areas, prove very competitive against established methods.