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001-es BibID:	BIBFORM123669
Első szerző:	Gandhi, Herry Kartika (industrial engineer)
Cím:	Multi-step Natural Gas Price Forecasting using Ensemble Empirical Mode Decomposition and Long Short-Term Memory Hybrid Model / Gandhi, Herry Kartika; Márton, Ispány
Dátum:	2024
ISSN:	2146-4553
Megjegyzések:	With the characteristic of natural gas as a clean, non-toxic, and valuable energy source, its use has been increasing in recent years. Thus, maintaining stable natural gas security requires a reliable long-step price forecasting indicator with less error. We propose a hybrid theory of Ensemble Empirical Mode Decomposition (EEMD) with Long Short-Term Memory (LSTM) to perform multi-step forecasting focusing on 30-90 steps of the daily Henry Hub natural gas price as a dataset. Using four widespread error measurements, the proposed model provides excellent results compared to no decomposition as the benchmark model. The proposed model provides 50% lower error results than the single LSTM. EEMD_LSTM brings values below 10 in the MAPE indicator, even up to 90-step prediction. The Diebold-Mariano test also confirms that EEMD_LSTM outperforms the single LSTM on every step with the majority of 90% confidence level. We also simulated the model by analysing the box and whiskers plot of RMSE, which shows that the variance of predicted values ranges between 1.11%. These results show that the proposed forecasting model provides robust results for the case of medium-term natural gas prices with excellent forecasting results.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Natural Gas Price Hybrid Forecasting EEMD Decomposition LSTM
Megjelenés:	International Journal of Energy Economics and Policy. - 14 : 4 (2024), p. 590-598. -
További szerzők:	Ispány Márton (1966-) (informatikus, matematikus)
Pályázati támogatás:	Stipendium Hungaricum Scholarship Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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