short-paper

Forecasting Seasonal Time Series Using Weighted Gradient RBF Network based Autoregressive Model

Authors:

Nguyen Khoi Tran,

Nickolas J.G. Falkner,

Wei Emma ZhangAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2021 - 2024

https://doi.org/10.1145/2983323.2983899

Published: 24 October 2016 Publication History

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.

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Cited By

Yuan LLi BLi SWong KZhang RQu H(2023)Tax-Scheduler: An interactive visualization system for staff shifting and scheduling at tax authoritiesVisual Informatics10.1016/j.visinf.2023.02.0017:2(30-40)Online publication date: Jun-2023
https://doi.org/10.1016/j.visinf.2023.02.001
Li CJiang HGe Q(2019)Power Data Cleaning in Micro Grid2019 Chinese Control Conference (CCC)10.23919/ChiCC.2019.8865726(3776-3781)Online publication date: Jul-2019
https://doi.org/10.23919/ChiCC.2019.8865726
Suhartono Amalia FSaputri PRahayu SSuprih Ulama B(2018)Simulation Study for Determining the Best Architecture of Multilayer Perceptron for Forecasting Nonlinear Seasonal Time SeriesJournal of Physics: Conference Series10.1088/1742-6596/1028/1/0122141028(012214)Online publication date: 14-Jun-2018
https://doi.org/10.1088/1742-6596/1028/1/012214

Index Terms

Forecasting Seasonal Time Series Using Weighted Gradient RBF Network based Autoregressive Model
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Data streaming
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

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Published: 24 October 2016

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October 24 - 28, 2016

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Yuan LLi BLi SWong KZhang RQu H(2023)Tax-Scheduler: An interactive visualization system for staff shifting and scheduling at tax authoritiesVisual Informatics10.1016/j.visinf.2023.02.0017:2(30-40)Online publication date: Jun-2023
https://doi.org/10.1016/j.visinf.2023.02.001
Li CJiang HGe Q(2019)Power Data Cleaning in Micro Grid2019 Chinese Control Conference (CCC)10.23919/ChiCC.2019.8865726(3776-3781)Online publication date: Jul-2019
https://doi.org/10.23919/ChiCC.2019.8865726
Suhartono Amalia FSaputri PRahayu SSuprih Ulama B(2018)Simulation Study for Determining the Best Architecture of Multilayer Perceptron for Forecasting Nonlinear Seasonal Time SeriesJournal of Physics: Conference Series10.1088/1742-6596/1028/1/0122141028(012214)Online publication date: 14-Jun-2018
https://doi.org/10.1088/1742-6596/1028/1/012214

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