Crude oil price forecasting by CEEMDAN based hybrid model of ARIMA and Kalman filter
This paper used complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) based hybrid model for the forecasting of world crude oil prices. For this purpose, the crude oil prices original time series are decomposed into sub small finite series called intrinsic mode functions (IMFs...
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Online Access: | http://eprints.utm.my/id/eprint/79708/1/AniShabri2018_CrudeOilPriceForecastingbyCeemdan.pdf http://eprints.utm.my/id/eprint/79708/ http://dx.doi.org/10.11113/jt.v80.10852 |
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my.utm.797082019-01-28T06:38:20Z http://eprints.utm.my/id/eprint/79708/ Crude oil price forecasting by CEEMDAN based hybrid model of ARIMA and Kalman filter Aamir, M. Shabri, A. Ishaq, M. QA Mathematics This paper used complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) based hybrid model for the forecasting of world crude oil prices. For this purpose, the crude oil prices original time series are decomposed into sub small finite series called intrinsic mode functions (IMFs). Then ARIMA model was applied to each extracted IMF to estimate the parameters. Next, using these estimated parameters of each ARIMA model, the Kalman Filter was run for each IMF, so that these extracted IMFs can be predicted more accurately. Finally, all IMFs are combined to get the result. For testing and verification of the proposed method, two crude oil prices were used as a sample i.e. Brent and WTI (West Texas Intermediate) crude oil monthly prices series. The D-statistic values of the proposed model were 93.33% for Brent and 89.29% for WTI which reveals the importance of the CEEMDAN based hybrid model. Penerbit UTM Press 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/79708/1/AniShabri2018_CrudeOilPriceForecastingbyCeemdan.pdf Aamir, M. and Shabri, A. and Ishaq, M. (2018) Crude oil price forecasting by CEEMDAN based hybrid model of ARIMA and Kalman filter. Jurnal Teknologi, 80 (4). pp. 67-79. ISSN 0127-9696 http://dx.doi.org/10.11113/jt.v80.10852 DOI:10.11113/jt.v80.10852 |
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QA Mathematics Aamir, M. Shabri, A. Ishaq, M. Crude oil price forecasting by CEEMDAN based hybrid model of ARIMA and Kalman filter |
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This paper used complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) based hybrid model for the forecasting of world crude oil prices. For this purpose, the crude oil prices original time series are decomposed into sub small finite series called intrinsic mode functions (IMFs). Then ARIMA model was applied to each extracted IMF to estimate the parameters. Next, using these estimated parameters of each ARIMA model, the Kalman Filter was run for each IMF, so that these extracted IMFs can be predicted more accurately. Finally, all IMFs are combined to get the result. For testing and verification of the proposed method, two crude oil prices were used as a sample i.e. Brent and WTI (West Texas Intermediate) crude oil monthly prices series. The D-statistic values of the proposed model were 93.33% for Brent and 89.29% for WTI which reveals the importance of the CEEMDAN based hybrid model. |
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Article |
author |
Aamir, M. Shabri, A. Ishaq, M. |
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Aamir, M. Shabri, A. Ishaq, M. |
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Aamir, M. |
title |
Crude oil price forecasting by CEEMDAN based hybrid model of ARIMA and Kalman filter |
title_short |
Crude oil price forecasting by CEEMDAN based hybrid model of ARIMA and Kalman filter |
title_full |
Crude oil price forecasting by CEEMDAN based hybrid model of ARIMA and Kalman filter |
title_fullStr |
Crude oil price forecasting by CEEMDAN based hybrid model of ARIMA and Kalman filter |
title_full_unstemmed |
Crude oil price forecasting by CEEMDAN based hybrid model of ARIMA and Kalman filter |
title_sort |
crude oil price forecasting by ceemdan based hybrid model of arima and kalman filter |
publisher |
Penerbit UTM Press |
publishDate |
2018 |
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http://eprints.utm.my/id/eprint/79708/1/AniShabri2018_CrudeOilPriceForecastingbyCeemdan.pdf http://eprints.utm.my/id/eprint/79708/ http://dx.doi.org/10.11113/jt.v80.10852 |
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