Open AccessEVALUATION OF COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR DAILY PRODUCT SALES FORECASTING
Author(s) -
Gediminas Žylius,
Rimvydas Simutis,
V. Vaitkus
Publication year - 2015
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.14.3.814
Subject(s) - sales forecasting , computer science , product (mathematics) , task (project management) , time series , set (abstract data type) , probabilistic forecasting , demand forecasting , series (stratigraphy) , forecast period , operations research , artificial intelligence , machine learning , econometrics , mathematics , production (economics) , engineering , economics , paleontology , geometry , macroeconomics , net present value , probabilistic logic , biology , programming language , systems engineering
Product sales forecasting is crucial task in inventory control and whole supply chain management. Accuracy of sales forecasting determines product logistics performance. In this paper we present study that aims to answer three questions: what input set is most informative for daily sales time series forecasting; do weather input features improve forecasting performance; what computational intelligence model is most appropriate for daily sales forecasting. In order to answer those questions we selected three computational intelligence models that are used for regression task together with various input sets for daily time series forecasting. Data collected consist of 89 real life product sales time series from various stores with historical period of 15 months. Results show that most useful input set is extracted from time series itself. Secondly, research results show that weather features do not improve forecasting performance. And finally, best forecasting results are achieved using support vector regression model.