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Forecasting seasonal time series using weighted gradient RBF network based autoregressive model

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  • Wenjie Ruan
  • Quan Z. Sheng
  • Peipei Xu
  • Nguyen Khoi Tran
  • Nickolas J.G. Falkner
  • Xue Li
  • Wei Emma Zhang
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Publication date24/10/2016
Host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery (ACM)
Pages2021-2024
Number of pages4
ISBN (Electronic)9781450340731
<mark>Original language</mark>English
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: 24/10/201628/10/2016

Conference

Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States
CityIndianapolis
Period24/10/1628/10/16

Conference

Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States
CityIndianapolis
Period24/10/1628/10/16

Abstract

How to accurately forecast seasonal time series is very important for many business area such as marketing decision, planning production and profit estimation. In this paper, we propose a weighted gradient Radial Basis Function Network based AutoRegressive (WGRBF-AR) model for modeling and predicting the nonlinear and non-stationary seasonal time series. This WGRBF-AR model is a synthesis of the weighted gradient RBF network and the functional-coefficient autoregressive (FAR) model through using the WGRBF networks to approximate varying coefficients of FAR model. It not only takes the advantages of the FAR model in nonlinear dynamics description but also inherits the capability of the WGRBF network to deal with non-stationarity. We test our model using ten-years retail sales data on five different commodity in US. The results demonstrate that the proposed WGRBF-AR model can achieve competitive prediction accuracy compared with the state-of-the-art.