@inproceedings{05d67b8e5c474e05877105c49f8c0967,
title = "Improving prediction in TAC SCM by integrating multivariate and temporal aspects via PLS regression",
abstract = "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.",
keywords = "feature selection, machine learning, prediction, price modeling, regression",
author = "William Groves and Maria Gini",
note = "Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2nd International Workshop on Trading Agent Design and Analysis, TADA 2011, Co-located with the 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 ; Conference date: 17-07-2011 Through 17-07-2011",
year = "2013",
doi = "10.1007/978-3-642-34889-1_3",
language = "English (US)",
isbn = "9783642348884",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer Verlag",
pages = "28--43",
booktitle = "Agent-Mediated Electronic Commerce - Designing Trading Strategies and Mechanisms for Electronic Markets, TADA 2011, Revised Selected Papers",
}