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Neural network versus classical time series forecasting models
Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network...
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| container_volume | 1842 |
| creator | Nor, Maria Elena Safuan, Hamizah Mohd Shab, Noorzehan Fazahiyah Md Asrul, Mohd Abdullah, Affendi Mohamad, Nurul Asmaa Izzati Lee, Muhammad Hisyam |
| description | Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing. |
| doi_str_mv | 10.1063/1.4982865 |
| format | conference_proceeding |
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This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. 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It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.</description><subject>Artificial neural networks</subject><subject>Autoregressive models</subject><subject>Forecasting</subject><subject>Forecasting techniques</subject><subject>Gold</subject><subject>Neural networks</subject><subject>Preprocessing</subject><subject>Time series</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2017</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kEtLAzEcxIMoWKsHv8GCN2Fr_nlsNhdBii8oelHwFtI8JHW7WZNsxW9vpQVvnuYwv5mBQegc8AxwQ69gxmRL2oYfoAlwDrVooDlEE4wlqwmjb8foJOcVxkQK0U7Q9ZMbk-6q3pWvmD6qjUt5zJXpdM7BbI0S1q7KLgWXKx-TMzqX0L9X62hdl0_Rkddddmd7naLXu9uX-UO9eL5_nN8s6oFwWmppvabOGyuNsEKC9ZZzwrAmkpElNJYumaWCeilb2jgsQAN1nGlPW2esoFN0sesdUvwcXS5qFcfUbycVAcKY5CDaLXW5o7IJRZcQezWksNbpW21iUqD236jB-v9gwOr3zL8A_QG8NWag</recordid><startdate>20170512</startdate><enddate>20170512</enddate><creator>Nor, Maria Elena</creator><creator>Safuan, Hamizah Mohd</creator><creator>Shab, Noorzehan Fazahiyah Md</creator><creator>Asrul, Mohd</creator><creator>Abdullah, Affendi</creator><creator>Mohamad, Nurul Asmaa Izzati</creator><creator>Lee, Muhammad Hisyam</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20170512</creationdate><title>Neural network versus classical time series forecasting models</title><author>Nor, Maria Elena ; Safuan, Hamizah Mohd ; Shab, Noorzehan Fazahiyah Md ; Asrul, Mohd ; Abdullah, Affendi ; Mohamad, Nurul Asmaa Izzati ; Lee, Muhammad Hisyam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p253t-9dfa3efcd9c7d791dfd55240a2942b16d3b4d373f99836e071a13e54af38ecd73</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Artificial neural networks</topic><topic>Autoregressive models</topic><topic>Forecasting</topic><topic>Forecasting techniques</topic><topic>Gold</topic><topic>Neural networks</topic><topic>Preprocessing</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nor, Maria Elena</creatorcontrib><creatorcontrib>Safuan, Hamizah Mohd</creatorcontrib><creatorcontrib>Shab, Noorzehan Fazahiyah Md</creatorcontrib><creatorcontrib>Asrul, Mohd</creatorcontrib><creatorcontrib>Abdullah, Affendi</creatorcontrib><creatorcontrib>Mohamad, Nurul Asmaa Izzati</creatorcontrib><creatorcontrib>Lee, Muhammad Hisyam</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nor, Maria Elena</au><au>Safuan, Hamizah Mohd</au><au>Shab, Noorzehan Fazahiyah Md</au><au>Asrul, Mohd</au><au>Abdullah, Affendi</au><au>Mohamad, Nurul Asmaa Izzati</au><au>Lee, Muhammad Hisyam</au><au>Yunus, Rossita Mohamad</au><au>Mohamed, Ibrahim</au><au>Bakar, Shaiful Anuar Abu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural network versus classical time series forecasting models</atitle><btitle>AIP conference proceedings</btitle><date>2017-05-12</date><risdate>2017</risdate><volume>1842</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. 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| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2017, Vol.1842 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_1_4982865 |
| source | American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list) |
| subjects | Artificial neural networks Autoregressive models Forecasting Forecasting techniques Gold Neural networks Preprocessing Time series |
| title | Neural network versus classical time series forecasting models |
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