A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area

A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area

© 2016 The Authors. Most of time series model are usually investigated and implemented by ARIMA and Neural Networks (NNs) model. However, ARIMA model may not be adequate for complex patterned problem while NNs model can well reveal the correlation of nonlinear pattern. Since, over-fitting due to a l...

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Main Authors: Wongsathan R., Seedadan I.
Format: Conference Proceeding
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84999633655&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/42175
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-421752017-09-28T04:25:38Z A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area Wongsathan R. Seedadan I. © 2016 The Authors. Most of time series model are usually investigated and implemented by ARIMA and Neural Networks (NNs) model. However, ARIMA model may not be adequate for complex patterned problem while NNs model can well reveal the correlation of nonlinear pattern. Since, over-fitting due to a learning process is the main advantage of NNs as well as local trapped of parameters due to the large structure of the networks. To improve the forecast performance of both ARIMA and NNs for high accuracy, hybrid ARIMA and NNs model is alternate selected and employed to examine the Chiangmai city moat's PM-10 time series data. The experimental results demonstrated that the hybrid model outperformed best over NNs and ARIMA respectively. 2017-09-28T04:25:38Z 2017-09-28T04:25:38Z 2016-01-01 Conference Proceeding 2-s2.0-84999633655 10.1016/j.procs.2016.05.057 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84999633655&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42175
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description © 2016 The Authors. Most of time series model are usually investigated and implemented by ARIMA and Neural Networks (NNs) model. However, ARIMA model may not be adequate for complex patterned problem while NNs model can well reveal the correlation of nonlinear pattern. Since, over-fitting due to a learning process is the main advantage of NNs as well as local trapped of parameters due to the large structure of the networks. To improve the forecast performance of both ARIMA and NNs for high accuracy, hybrid ARIMA and NNs model is alternate selected and employed to examine the Chiangmai city moat's PM-10 time series data. The experimental results demonstrated that the hybrid model outperformed best over NNs and ARIMA respectively.
format Conference Proceeding
author Wongsathan R.
Seedadan I.
spellingShingle Wongsathan R.
Seedadan I.
A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area
author_facet Wongsathan R.
Seedadan I.
author_sort Wongsathan R.
title A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area
title_short A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area
title_full A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area
title_fullStr A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area
title_full_unstemmed A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area
title_sort hybrid arima and neural networks model for pm-10 pollution estimation: the case of chiang mai city moat area
publishDate 2017
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84999633655&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/42175
_version_ 1681422139591229440

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