Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique
This paper introduces a new hybrid time series forecasting technique to obtain an efficient and accurate daily crude oil prices forecast. The proposed hybrid technique combines the features of various regression, time series, and machine learning models to improve forecast accuracy. First, it involv...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11017674/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849691647207538688 |
|---|---|
| author | Hasnain Iftikhar Moiz Qureshi Paulo Canas Rodrigues Muhammad Usman Iftikhar Javier Linkolk Lopez-Gonzales Hasnain Iftikhar |
| author_facet | Hasnain Iftikhar Moiz Qureshi Paulo Canas Rodrigues Muhammad Usman Iftikhar Javier Linkolk Lopez-Gonzales Hasnain Iftikhar |
| author_sort | Hasnain Iftikhar |
| collection | DOAJ |
| description | This paper introduces a new hybrid time series forecasting technique to obtain an efficient and accurate daily crude oil prices forecast. The proposed hybrid technique combines the features of various regression, time series, and machine learning models to improve forecast accuracy. First, it involved processing the original crude oil prices time series to address missing values, variance stabilization, and normalization. Second, it divides the purified crude oil price time series into two major parts: the nonlinear long-term trend (long-run fluctuations) and the residual (short-term fluctuations). The secular non-linear trend series is modeled using various regression models, including linear, spline, lowess, and smoothing spline regression models. On the other hand, the short-run random part is modeled and forecasted using four benchmark time series (linear and non-linear AR, ARMA, ESM) and four machine learning (Neural network autoregressive, Random forest, support vector regression with polynomial and radial basal functions) models. The forecast from both parts is summed to obtain the final forecasting results. The proposed hybrid time series method builds a strong emphasis on effectively measuring the nonlinear long-term trend, which has been disregarded in prior works. A variety of evaluation metrics are employed to evaluate the proposed effectiveness. The testing findings demonstrate the hybrid forecasting technique’s accuracy and efficacy. Specifically, an MAE of 1.28106, an RMSE of 1.59259, a PCC of 0.94158, and a DS of 0.82149 are obtained for the Brent and WTI oil markets when the Lowess regression and NPAR models are combined. With regard to WTI oil prices in particular, the model’s resilience in producing precise forecasts is further demonstrated by the lowest MAE of 1.25730, RMSE of 1.55390, and PCC of 0.93890. |
| format | Article |
| id | doaj-art-e77df39f97774c4699128f169760cff1 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e77df39f97774c4699128f169760cff12025-08-20T03:20:58ZengIEEEIEEE Access2169-35362025-01-0113988229883610.1109/ACCESS.2025.357478811017674Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series TechniqueHasnain Iftikhar0https://orcid.org/0000-0002-8533-5410Moiz Qureshi1Paulo Canas Rodrigues2https://orcid.org/0000-0002-1248-9910Muhammad Usman Iftikhar3Javier Linkolk Lopez-Gonzales4https://orcid.org/0000-0003-0847-0552Hasnain Iftikhar5Department of Statistics, University of Peshawar, Peshawar, PakistanDepartment of Statistics, Quaid-i-Azam University, Islamabad, PakistanDepartment of Statistics, Federal University of Bahia, Salvador, BrazilFaculty of Science, Engineering and Built Environment, Deakin University, Burwood, AustraliaEscuela de Posgrado, Universidad Peruana Unión, Lima, PeruFaculty of Science, Engineering and Built Environment, Deakin University, Burwood, AustraliaThis paper introduces a new hybrid time series forecasting technique to obtain an efficient and accurate daily crude oil prices forecast. The proposed hybrid technique combines the features of various regression, time series, and machine learning models to improve forecast accuracy. First, it involved processing the original crude oil prices time series to address missing values, variance stabilization, and normalization. Second, it divides the purified crude oil price time series into two major parts: the nonlinear long-term trend (long-run fluctuations) and the residual (short-term fluctuations). The secular non-linear trend series is modeled using various regression models, including linear, spline, lowess, and smoothing spline regression models. On the other hand, the short-run random part is modeled and forecasted using four benchmark time series (linear and non-linear AR, ARMA, ESM) and four machine learning (Neural network autoregressive, Random forest, support vector regression with polynomial and radial basal functions) models. The forecast from both parts is summed to obtain the final forecasting results. The proposed hybrid time series method builds a strong emphasis on effectively measuring the nonlinear long-term trend, which has been disregarded in prior works. A variety of evaluation metrics are employed to evaluate the proposed effectiveness. The testing findings demonstrate the hybrid forecasting technique’s accuracy and efficacy. Specifically, an MAE of 1.28106, an RMSE of 1.59259, a PCC of 0.94158, and a DS of 0.82149 are obtained for the Brent and WTI oil markets when the Lowess regression and NPAR models are combined. With regard to WTI oil prices in particular, the model’s resilience in producing precise forecasts is further demonstrated by the lowest MAE of 1.25730, RMSE of 1.55390, and PCC of 0.93890.https://ieeexplore.ieee.org/document/11017674/Crude oil price forecastingregression modelstime series modelsmachine learning modelshybrid time series forecasting techniquedecision making |
| spellingShingle | Hasnain Iftikhar Moiz Qureshi Paulo Canas Rodrigues Muhammad Usman Iftikhar Javier Linkolk Lopez-Gonzales Hasnain Iftikhar Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique IEEE Access Crude oil price forecasting regression models time series models machine learning models hybrid time series forecasting technique decision making |
| title | Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique |
| title_full | Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique |
| title_fullStr | Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique |
| title_full_unstemmed | Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique |
| title_short | Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique |
| title_sort | daily crude oil prices forecasting using a novel hybrid time series technique |
| topic | Crude oil price forecasting regression models time series models machine learning models hybrid time series forecasting technique decision making |
| url | https://ieeexplore.ieee.org/document/11017674/ |
| work_keys_str_mv | AT hasnainiftikhar dailycrudeoilpricesforecastingusinganovelhybridtimeseriestechnique AT moizqureshi dailycrudeoilpricesforecastingusinganovelhybridtimeseriestechnique AT paulocanasrodrigues dailycrudeoilpricesforecastingusinganovelhybridtimeseriestechnique AT muhammadusmaniftikhar dailycrudeoilpricesforecastingusinganovelhybridtimeseriestechnique AT javierlinkolklopezgonzales dailycrudeoilpricesforecastingusinganovelhybridtimeseriestechnique AT hasnainiftikhar dailycrudeoilpricesforecastingusinganovelhybridtimeseriestechnique |