Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108661
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Type: Conference paper
Title: Forecasting seasonal time series using weighted gradient RBF network based autoregressive model
Author: Ruan, W.
Sheng, Q.
Xu, P.
Tran, N.
Falkner, N.
Li, X.
Zhang, W.
Citation: Proceedings of the 25th International Conference on Information and Knowledge Management, 2016 / vol.24-28-October-2016, pp.2021-2024
Publisher: ACM
Issue Date: 2016
ISBN: 9781450340731
Conference Name: 25th ACM International Conference on Information and Knowledge Management (CIKM) (24 Oct 2016 - 28 Oct 2016 : Indianapolis, IN)
Statement of
Responsibility: 
Wenjie Ruan, Quan Z. Sheng, Peipei Xu, Nguyen Khoi Tran, Xue Li, Wei Emma Zhang
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.
Rights: © 2016 ACM
RMID: 0030059267
DOI: 10.1145/2983323.2983899
Appears in Collections:Computer Science publications

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