PSO based Neural Networks vs. traditional statistical models for seasonal time series forecasting
Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to...
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description | Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models. |
doi_str_mv | 10.1109/IAdCC.2013.6514315 |
format | Conference Proceeding |
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K. ; Kant, L.</creator><creatorcontrib>Adhikari, R. ; Agrawal, R. K. ; Kant, L.</creatorcontrib><description>Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. 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K.</creatorcontrib><creatorcontrib>Kant, L.</creatorcontrib><title>PSO based Neural Networks vs. traditional statistical models for seasonal time series forecasting</title><title>2013 3rd IEEE International Advance Computing Conference (IACC)</title><addtitle>IAdCC</addtitle><description>Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models.</description><subject>ANN</subject><subject>Artificial neural networks</subject><subject>Box-Jenkins models</subject><subject>Computational modeling</subject><subject>Elman ANN</subject><subject>Forecasting</subject><subject>particle swarm optimization</subject><subject>Predictive models</subject><subject>seasonality</subject><subject>Support vector machines</subject><subject>Time series analysis</subject><subject>time series forecasting</subject><subject>Training</subject><isbn>9781467345279</isbn><isbn>146734527X</isbn><isbn>9781467345293</isbn><isbn>1467345288</isbn><isbn>9781467345286</isbn><isbn>1467345296</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkM1OwzAQhI0QEqjkBeCSF0hY27FTH6uIn0oVRaL3amtvkCFpkG1AvD1W2wun2dlvdg7L2A2HmnMwd8uF67paAJe1VryRXJ2xwrRz3uhWNkoYef7Pt-aSFTG-A0C-11zDFcOX13W5w0iufKavgEOW9DOFj1h-x7pMAZ1PftpnEBMmH5O3eR4nR0Ms-ymUkTAeePIjZRc8HQBZzOH92zW76HGIVJx0xjYP95vuqVqtH5fdYlV5A6nSAgha1aIwVhqudr1ThEgaDFiTF-g0J-GEkkQgjDNyjppDa7ntHRo5Y7fHWk9E28_gRwy_29Nf5B-ygleX</recordid><startdate>201302</startdate><enddate>201302</enddate><creator>Adhikari, R.</creator><creator>Agrawal, R. K.</creator><creator>Kant, L.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201302</creationdate><title>PSO based Neural Networks vs. traditional statistical models for seasonal time series forecasting</title><author>Adhikari, R. ; Agrawal, R. K. ; Kant, L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-620e0757a29c3915bfd5eaae6090c9915ad61e2d253ee029d938a6107c1cfda93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>ANN</topic><topic>Artificial neural networks</topic><topic>Box-Jenkins models</topic><topic>Computational modeling</topic><topic>Elman ANN</topic><topic>Forecasting</topic><topic>particle swarm optimization</topic><topic>Predictive models</topic><topic>seasonality</topic><topic>Support vector machines</topic><topic>Time series analysis</topic><topic>time series forecasting</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Adhikari, R.</creatorcontrib><creatorcontrib>Agrawal, R. K.</creatorcontrib><creatorcontrib>Kant, L.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Adhikari, R.</au><au>Agrawal, R. K.</au><au>Kant, L.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>PSO based Neural Networks vs. traditional statistical models for seasonal time series forecasting</atitle><btitle>2013 3rd IEEE International Advance Computing Conference (IACC)</btitle><stitle>IAdCC</stitle><date>2013-02</date><risdate>2013</risdate><spage>719</spage><epage>725</epage><pages>719-725</pages><isbn>9781467345279</isbn><isbn>146734527X</isbn><eisbn>9781467345293</eisbn><eisbn>1467345288</eisbn><eisbn>9781467345286</eisbn><eisbn>1467345296</eisbn><abstract>Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models.</abstract><pub>IEEE</pub><doi>10.1109/IAdCC.2013.6514315</doi><tpages>7</tpages></addata></record> |
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subjects | ANN Artificial neural networks Box-Jenkins models Computational modeling Elman ANN Forecasting particle swarm optimization Predictive models seasonality Support vector machines Time series analysis time series forecasting Training |
title | PSO based Neural Networks vs. traditional statistical models for seasonal time series forecasting |
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