Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models
This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicte...
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| Veröffentlicht in: | Applied energy 2010-11, Vol.87 (11), p.3606-3610 |
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| container_end_page | 3610 |
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| container_title | Applied energy |
| container_volume | 87 |
| creator | Tan, Zhongfu Zhang, Jinliang Wang, Jianhui Xu, Jun |
| description | This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. |
| doi_str_mv | 10.1016/j.apenergy.2010.05.012 |
| format | Article |
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By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods.</description><subject>Applied sciences</subject><subject>Approximation</subject><subject>ARIMA</subject><subject>Composing</subject><subject>Economic data</subject><subject>Electric energy</subject><subject>Electricity</subject><subject>Energy</subject><subject>Energy economics</subject><subject>Exact sciences and technology</subject><subject>Forecasting</subject><subject>GARCH</subject><subject>General, economic and professional studies</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Methodology. Modelling</subject><subject>Price forecasting</subject><subject>Price forecasting Wavelet transform ARIMA GARCH</subject><subject>Time series</subject><subject>Wavelet transform</subject><subject>Wavelet transforms</subject><issn>0306-2619</issn><issn>1872-9118</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNqFkU1v2zAMho1hA5p1_QuFLsNOzijLlu3bgqxrC7QYUKxngZXpRoG_Jikp_O9HL12vO5ASxPclqIdJcilhLUHqr_s1TjSQf57XGfAjFGuQ2btkJasyS2spq_fJChToNNOyPks-hrAHgExmsEqa7zinuCNsBHVko3fWxVlMfJJoR08WQ3TDsziEJb_gkWVRRI9D4HIv7Ng_uYEa8eLiTmwebu83AodGXG8etjeiHxvqwqfkQ4tdoIvX8zx5_HH1a3uT3v28vt1u7lKb53lMi8pSnecABda6RF2WRa1qnVGV1wWUbaVQ5VbLskKoc1RoZQ1UqJbviuyTOk--nPpOfvx9oBBN74KlrsOBxkMwlQalK2bESn1SWj-G4Kk1_OMe_WwkmIWq2Zt_VM1C1UBhmCob709GTxPZNxcR4bTozdEorEpOM8dfp0LHISWniUNp0EZpLuxiz_0-v46MwWLXMlfrwlvfTMllUyXrvp10jJOOjrwJ1tFgqXG8o2ia0f1v9D8G86m5</recordid><startdate>20101101</startdate><enddate>20101101</enddate><creator>Tan, Zhongfu</creator><creator>Zhang, Jinliang</creator><creator>Wang, Jianhui</creator><creator>Xu, Jun</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TA</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20101101</creationdate><title>Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models</title><author>Tan, Zhongfu ; Zhang, Jinliang ; Wang, Jianhui ; Xu, Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c444t-58ce944005a967a677593962e849507f83a34c6178a094a3ac190e53f4a33ecb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Applied sciences</topic><topic>Approximation</topic><topic>ARIMA</topic><topic>Composing</topic><topic>Economic data</topic><topic>Electric energy</topic><topic>Electricity</topic><topic>Energy</topic><topic>Energy economics</topic><topic>Exact sciences and technology</topic><topic>Forecasting</topic><topic>GARCH</topic><topic>General, economic and professional studies</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Methodology. Modelling</topic><topic>Price forecasting</topic><topic>Price forecasting Wavelet transform ARIMA GARCH</topic><topic>Time series</topic><topic>Wavelet transform</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tan, Zhongfu</creatorcontrib><creatorcontrib>Zhang, Jinliang</creatorcontrib><creatorcontrib>Wang, Jianhui</creatorcontrib><creatorcontrib>Xu, Jun</creatorcontrib><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Materials Business File</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Applied energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tan, Zhongfu</au><au>Zhang, Jinliang</au><au>Wang, Jianhui</au><au>Xu, Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models</atitle><jtitle>Applied energy</jtitle><date>2010-11-01</date><risdate>2010</risdate><volume>87</volume><issue>11</issue><spage>3606</spage><epage>3610</epage><pages>3606-3610</pages><issn>0306-2619</issn><eissn>1872-9118</eissn><coden>APENDX</coden><abstract>This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.apenergy.2010.05.012</doi><tpages>5</tpages></addata></record> |
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| ispartof | Applied energy, 2010-11, Vol.87 (11), p.3606-3610 |
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| language | eng |
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| source | RePEc; Elsevier ScienceDirect Journals |
| subjects | Applied sciences Approximation ARIMA Composing Economic data Electric energy Electricity Energy Energy economics Exact sciences and technology Forecasting GARCH General, economic and professional studies Mathematical analysis Mathematical models Methodology. Modelling Price forecasting Price forecasting Wavelet transform ARIMA GARCH Time series Wavelet transform Wavelet transforms |
| title | Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models |
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