On the genetic programming of time-series predictors for supply chain management

Single and multi-step time-series predictors were evolved for forecasting minimum bidding prices in a simulated supply chain management scenario. Evolved programs were allowed to use primitives that facilitate the statistical analysis of historical data. An investigation of the relationships between the use of such primitives and the induction of both accurate and predictive solutions was made, with the statistics calculated based on three input data transformation methods: integral, differential, and rational. Results are presented showing which features work best for both single-step and multi-step predictions. Categories and Subject Descriptors I.2 [ARTIFICIAL INTELLIGENCE]: Automatic Programming General Terms Algorithms, Performance, Economics, Management Keywords Prediction/Forecasting, Statistical Time-Series Features, Single-Step Prediction, Iterated Single-Step Prediction

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Alexandros Agapitos, Matthew Dyson, Jenya Kovalchuk, Simon M. Lucas
• Keywords: GECCO 2008, Minimum Bidding Prices, Multi-step Time-series Predictors, Optimization, Single-Step Prediction