Improving prediction in TAC SCM by integrating multivariate and temporal aspects via PLS regression

William Groves, Maria Gini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAgent-Mediated Electronic Commerce - Designing Trading Strategies and Mechanisms for Electronic Markets, TADA 2011, Revised Selected Papers
PublisherSpringer- Verlag
Pages28-43
Number of pages16
ISBN (Print)9783642348884
DOIs
StatePublished - Jan 1 2013
Event2nd International Workshop on Trading Agent Design and Analysis, TADA 2011, Co-located with the 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Spain
Duration: Jul 17 2011Jul 17 2011

Publication series

NameLecture Notes in Business Information Processing
Volume119 LNBIP
ISSN (Print)1865-1348

Other

Other2nd International Workshop on Trading Agent Design and Analysis, TADA 2011, Co-located with the 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
CountrySpain
CityBarcelona
Period7/17/117/17/11

Keywords

  • feature selection
  • machine learning
  • prediction
  • price modeling
  • regression

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