Forecasting crude oil prices using reservoir computing models
Accurate forecasting of crude oil prices is crucial for informed financial decision-making. This study presents a cutting-edge Reservoir Computing (RC) model specifically designed for precise crude oil price predictions, outperforming traditional methods such as ARIMA, LSTM, and GRU. Using daily clo...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
2025
|
| In: |
Computational economics
Year: 2025, Volume: 66, Issue: 3, Pages: 2543-2563 |
| ISSN: | 1572-9974 |
| DOI: | 10.1007/s10614-024-10797-w |
| Subjects: | |
| Online Access: | Verlag, kostenfrei: https://link.springer.com/content/pdf/10.1007/s10614-024-10797-w.pdf Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s10614-024-10797-w 
|
| Author Notes: | Kaushal Kumar |
MARC
| LEADER | 00000caa a2200000 c 4500 | ||
|---|---|---|---|
| 001 | 1931000867 | ||
| 003 | DE-627 | ||
| 005 | 20260204132934.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 250717s2025 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10614-024-10797-w |2 doi | |
| 035 | |a (DE-627)1931000867 | ||
| 035 | |a (DE-599)KXP1931000867 | ||
| 035 | |a (OCoLC)1559703162 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a 27 |2 sdnb | ||
| 100 | 1 | |a Kumar, Kaushal |e VerfasserIn |0 (DE-588)130324425X |0 (DE-627)186016188X |4 aut | |
| 245 | 1 | 0 | |a Forecasting crude oil prices using reservoir computing models |c Kaushal Kumar |
| 264 | 1 | |c 2025 | |
| 300 | |b Illustrationen | ||
| 300 | |a 21 | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Veröffentlicht: 28 November 2024 | ||
| 500 | |a Gesehen am 17.07.2025 | ||
| 520 | |a Accurate forecasting of crude oil prices is crucial for informed financial decision-making. This study presents a cutting-edge Reservoir Computing (RC) model specifically designed for precise crude oil price predictions, outperforming traditional methods such as ARIMA, LSTM, and GRU. Using daily closing prices from major indices spanning January 2010 to December 2023, we conducted a thorough evaluation. The RC model consistently demonstrates superior accuracy and computational efficiency. Quantitative metrics reveal the RC model’s dominance with a Mean Absolute Error (MAE) of 0.0094, Mean Squared Error (MSE) of 0.00035, Root Mean Squared Error (RMSE) of 0.0196, and a notably low Mean Absolute Percentage Error (MAPE) of $$1.450\%$$. Additionally, the RC model’s runtime of 1.11 s underscores its computational efficiency, far surpassing ARIMA (493.22 s), LSTM (423.55 s), and GRU (15.73 s). During periods of economic disruption, such as the COVID-19 lockdowns, the RC model effectively captured sharp price fluctuations, highlighting its robust forecasting capability. These findings emphasize the RC model’s potential as a reliable tool for enhancing decision-making processes in the dynamic energy market, particularly for real-time applications such as infectious disease case count forecasting. This study advocates for the broader adoption of Reservoir Computing models to improve predictive accuracy and operational efficiency in energy economics. | ||
| 650 | 4 | |a Computational Economics | |
| 650 | 4 | |a Computational Intelligence | |
| 650 | 4 | |a Computational Neuroscience | |
| 650 | 4 | |a Computer Science | |
| 650 | 4 | |a Crude oil prices | |
| 650 | 4 | |a Energy Informatics | |
| 650 | 4 | |a Financial market prediction | |
| 650 | 4 | |a Forecasting | |
| 650 | 4 | |a Information Processing | |
| 650 | 4 | |a Reservoir computing | |
| 650 | 4 | |a Time series analysis | |
| 655 | 4 | |0 (DE-206)49 |a Aufsatz in Zeitschrift |5 DE-206 | |
| 773 | 0 | 8 | |i Enthalten in |t Computational economics |d Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 |g 66(2025), 3 vom: Sept., Seite 2543-2563 |h Online-Ressource |w (DE-627)270427546 |w (DE-600)1477445-8 |w (DE-576)121190374 |x 1572-9974 |7 nnas |a Forecasting crude oil prices using reservoir computing models |
| 773 | 1 | 8 | |g volume:66 |g year:2025 |g number:3 |g month:09 |g pages:2543-2563 |g extent:21 |a Forecasting crude oil prices using reservoir computing models |
| 856 | 4 | 0 | |u https://link.springer.com/content/pdf/10.1007/s10614-024-10797-w.pdf |q application/pdf |x Verlag |z kostenfrei |7 0 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10614-024-10797-w |x Verlag |x Resolving-System |z kostenfrei |3 Volltext |7 0 |
| 951 | |a AR | ||
| 992 | |a 20250717 | ||
| 993 | |a Article | ||
| 994 | |a 2025 | ||
| 998 | |g 130324425X |a Kumar, Kaushal |m 130324425X:Kumar, Kaushal |d 700000 |d 708000 |e 700000PK130324425X |e 708000PK130324425X |k 0/700000/ |k 1/700000/708000/ |p 1 |x j |y j | ||
| 999 | |a KXP-PPN1931000867 |e 4747179836 | ||
| BIB | |a Y | ||
| SER | |a journal | ||
| JSO | |a {"relHost":[{"pubHistory":["1.1988 -"],"recId":"270427546","note":["Gesehen am 16.01.08"],"disp":"Forecasting crude oil prices using reservoir computing modelsComputational economics","part":{"volume":"66","pages":"2543-2563","text":"66(2025), 3 vom: Sept., Seite 2543-2563","year":"2025","extent":"21","issue":"3"},"title":[{"title_sort":"Computational economics","title":"Computational economics"}],"titleAlt":[{"title":"Computer science in economics and management"}],"physDesc":[{"extent":"Online-Ressource"}],"origin":[{"dateIssuedKey":"1988","publisher":"Springer Science + Business Media B.V. ; Proquest ; Kluwer Academic Publishers","dateIssuedDisp":"1988-","publisherPlace":"Dordrecht [u.a.] ; [Erscheinungsort nicht ermittelbar] ; Dordrecht [u.a.]"}],"id":{"eki":["270427546"],"issn":["1572-9974"],"zdb":["1477445-8"]},"language":["eng"],"type":{"media":"Online-Ressource","bibl":"periodical"}}],"person":[{"family":"Kumar","display":"Kumar, Kaushal","given":"Kaushal","role":"aut"}],"physDesc":[{"noteIll":"Illustrationen","extent":"21 S."}],"title":[{"title":"Forecasting crude oil prices using reservoir computing models","title_sort":"Forecasting crude oil prices using reservoir computing models"}],"id":{"doi":["10.1007/s10614-024-10797-w"],"eki":["1931000867"]},"language":["eng"],"type":{"media":"Online-Ressource","bibl":"article-journal"},"origin":[{"dateIssuedDisp":"2025","dateIssuedKey":"2025"}],"note":["Veröffentlicht: 28 November 2024","Gesehen am 17.07.2025"],"recId":"1931000867","name":{"displayForm":["Kaushal Kumar"]}} | ||
| SRT | |a KUMARKAUSHFORECASTIN2025 | ||