Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NN
In order to get the excellent accuracy for price forecast in the cell phone market, a novel improved Sliding Window (SW) model based on adaptive windows width and a novel improved Radial Basis Function (RBF) Neural Network (NN) model based on adaptive spread are proposed and the Disturbance Factors...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2013-12-01
|
Series: | Journal of Algorithms & Computational Technology |
Online Access: | https://doi.org/10.1260/1748-3018.7.4.395 |
id |
doaj-f7fa5ddbe0f04e5abd7b45a39443dffb |
---|---|
record_format |
Article |
spelling |
doaj-f7fa5ddbe0f04e5abd7b45a39443dffb2020-11-25T03:20:53ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30181748-30262013-12-01710.1260/1748-3018.7.4.395Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NNQuan-Yin Zhu0Su-Qun Cao1Pei Zhou2Yonghua Yin3 Faculty of Computer Engineering, Huaiyin Institute of Technology, 223003, China Faculty of Mechanical Engineering, Huaiyin Institute of Technology, 223003, China Faculty of Computer Engineering, Huaiyin Institute of Technology, 223003, China Faculty of Computer Engineering, Huaiyin Institute of Technology, 223003, ChinaIn order to get the excellent accuracy for price forecast in the cell phone market, a novel improved Sliding Window (SW) model based on adaptive windows width and a novel improved Radial Basis Function (RBF) Neural Network (NN) model based on adaptive spread are proposed and the Disturbance Factors Model (DFM) is used in this paper. All of the three kinds of price forecasting models are utilized to verify the accuracy. The cell phone price is extracted from different websites and used as the model verification data. And the experimental results of the forecasting average accuracy based on the DFM obtain 94.61 percent. The experimental results of the forecasting average accuracy based on the ARBF NN model obtain 97.88 percent. The experimental results of the forecasting average accuracy based on the Adaptive SW Model (ASWM) obtain 99.64 percent. Although the results based on the DFM are not very good, it is still a satisfactory result. Since it is at least not a very serious result which proves that it is worth to do further researches in the field of the cell phone market based on the DFM. The results based on the ASWM and the ARBF NN models are satisfied. The improved methods enhance the forecast accuracy compared to the original model. In the field of the price forecast on the cell phone market, the improved methods have a good performance which is valuable and useful not only for businesses, but also for consumers.https://doi.org/10.1260/1748-3018.7.4.395 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Quan-Yin Zhu Su-Qun Cao Pei Zhou Yonghua Yin |
spellingShingle |
Quan-Yin Zhu Su-Qun Cao Pei Zhou Yonghua Yin Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NN Journal of Algorithms & Computational Technology |
author_facet |
Quan-Yin Zhu Su-Qun Cao Pei Zhou Yonghua Yin |
author_sort |
Quan-Yin Zhu |
title |
Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NN |
title_short |
Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NN |
title_full |
Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NN |
title_fullStr |
Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NN |
title_full_unstemmed |
Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NN |
title_sort |
price forecasting for cell phone market using adaptive sliding window and adaptive rbf nn |
publisher |
SAGE Publishing |
series |
Journal of Algorithms & Computational Technology |
issn |
1748-3018 1748-3026 |
publishDate |
2013-12-01 |
description |
In order to get the excellent accuracy for price forecast in the cell phone market, a novel improved Sliding Window (SW) model based on adaptive windows width and a novel improved Radial Basis Function (RBF) Neural Network (NN) model based on adaptive spread are proposed and the Disturbance Factors Model (DFM) is used in this paper. All of the three kinds of price forecasting models are utilized to verify the accuracy. The cell phone price is extracted from different websites and used as the model verification data. And the experimental results of the forecasting average accuracy based on the DFM obtain 94.61 percent. The experimental results of the forecasting average accuracy based on the ARBF NN model obtain 97.88 percent. The experimental results of the forecasting average accuracy based on the Adaptive SW Model (ASWM) obtain 99.64 percent. Although the results based on the DFM are not very good, it is still a satisfactory result. Since it is at least not a very serious result which proves that it is worth to do further researches in the field of the cell phone market based on the DFM. The results based on the ASWM and the ARBF NN models are satisfied. The improved methods enhance the forecast accuracy compared to the original model. In the field of the price forecast on the cell phone market, the improved methods have a good performance which is valuable and useful not only for businesses, but also for consumers. |
url |
https://doi.org/10.1260/1748-3018.7.4.395 |
work_keys_str_mv |
AT quanyinzhu priceforecastingforcellphonemarketusingadaptiveslidingwindowandadaptiverbfnn AT suquncao priceforecastingforcellphonemarketusingadaptiveslidingwindowandadaptiverbfnn AT peizhou priceforecastingforcellphonemarketusingadaptiveslidingwindowandadaptiverbfnn AT yonghuayin priceforecastingforcellphonemarketusingadaptiveslidingwindowandadaptiverbfnn |
_version_ |
1724616032769802240 |