Improving Prediction in TAC SCM by Integrating Multivariate and Temporal Aspects via PLS Regression

摘要

We address the construction of a prediction model from data available in a complex environment. We first present a data extraction method that is able to leverage information contained in the movements of all variables in recent observations. This improved data extraction is then used with a common multivariate regression technique: Partial Least Squares (PLS) regression. We experimentally validate this combined data extraction and modeling with data from a competitive multi-agent supply chain setting, the Trading Agent Competition for Supply Chain Management (TAC SCM). Our method achieves competitive (and often superior) performance compared to the state-of-the-art domain-specific prediction techniques used in the 2008 Prediction Challenge competition.