A novel hybrid model for forecasting crude oil price based on time series decomposition

Abdollahi, Hooman

doi:10.1016/j.apenergy.2020.115035

Cited by 95 publications

(36 citation statements)

References 60 publications

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“…It starts from the characteristics of data itself and reveals the internal fluctuation characteristics of data by decomposing the fluctuation information of original signal on different scales. Some researches [19][20][21][22][23] have demonstrated that it is an effective time series analysis tool and applied it to price forecasting.…”

Section: Related Workmentioning

confidence: 99%

Crude oil price prediction using CEEMDAN and LSTM-attention with news sentiment index

2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Crude oil is one of the most powerful types of energy and the fluctuation of its price influences the global economy. Therefore, building a scientific model to accurately predict the price of crude oil is significant for investors, governments and researchers. However, the nonlinearity and nonstationarity of crude oil prices make it a challenging task for forecasting time series accurately. To handle the issue, this paper proposed a novel forecasting approach for crude oil prices that combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Long Short-Term Memory (LSTM) with attention mechanism and addition, following the well-known “decomposition and ensemble” framework. In addition, a news sentiment index based on Chinese crude oil news texts was constructed and added to the prediction of crude oil prices. And we made full use of attention mechanism to better integrate price series and sentiment series according to the characteristics of each component. To validate the performance of the proposed CEEMDAN-LSTM_att-ADD, we selected the Mean Absolute Percent Error (MAPE), the Root Mean Squared Error (RMSE) and the Diebold-Mariano (DM) statistic as evaluation criterias. Abundant experiments were conducted on West Texas Intermediate (WTI) spot crude oil prices. The proposed approach outperformed several state-of-the-art methods for forecasting crude oil prices, which proved the effectiveness of the CEEMDAN-LSTM_att-ADD with the news sentiment index.

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Section: Related Workmentioning

confidence: 99%

Crude oil price prediction using CEEMDAN and LSTM-attention with news sentiment index

2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…The crucial part of NNAR modeling is to find the appropriate values for p and k lagged inputs. In this work, Akaike's information criterion (AIC) [21]- [23] was used to automate the parameter selection process using R programming language. In fact, this method is asymptotically equivalent to cross-validation [23].…”

Section: Algorithmmentioning

confidence: 99%

“…In this work, Akaike's information criterion (AIC) [21]- [23] was used to automate the parameter selection process using R programming language. In fact, this method is asymptotically equivalent to cross-validation [23]. The best model with p and k was then chosen with the least value of AIC using the R language.…”

Section: Algorithmmentioning

confidence: 99%

Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast

et al. 2021

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The demand for high steady state network traffic utilization is growing exponentially. Therefore, traffic forecasting has become essential for powering greedy application and services such as the internet of things (IoT) and Big data for 5G networks for better resource planning, allocation, and optimization. The accuracy of forecasting modeling has become crucial for fundamental network operations such as routing management, congestion management, and to guarantee quality of service overall. In this paper, a hybrid network forecast model was analyzed; the model combines a non-linear auto regressive neural network (NARNN) and various smoothing techniques, namely, local regression (LOESS), moving average, locally weighted scatterplot smoothing (LOWESS), the Sgolay filter, Robyn loess (RLOESS), and robust locally weighted scatterplot smoothing (RLOWESS). The effects of applying smoothing techniques with varied smoothing windows were shown and the performance of the hybrid NARNN and smoothing techniques discussed. The results show that the hybrid model can effectively be used to enhance forecasting performance in terms of forecasting accuracy, with the assistance of the smoothing techniques, which minimized data losses. In this work, root mean square error (RMSE) is used as performance measures and the results were verified via statistical significance tests.

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“…Kaijian et al [31] developed a method for forecasting electricity market risk using Empirical Mode decomposition (EMD) based on the Value at Risk (VaR) model, with Exponential Weighted Moving Average (EWMA) representing individual risk factors. Separately, decomposition-based TSF methods such as a multiobjective optimization for short-term wind speed forecasting [32], an ensemble empirical mode decomposition based crude oil price forecasting [33], as well as AI-based models that use deep recurrent neural networks [34], long short term memory networks [35], and hybrid neuro-fuzzy inference [36] for energy consumption prediction were reported in the recent literature.…”

Section: Related Workmentioning

confidence: 99%

Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing

et al. 2021

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Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the particle swarm optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of multi-step price prediction of the Australian electricity market.

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A novel hybrid model for forecasting crude oil price based on time series decomposition

Cited by 95 publications

References 60 publications

Crude oil price prediction using CEEMDAN and LSTM-attention with news sentiment index

Crude oil price prediction using CEEMDAN and LSTM-attention with news sentiment index

Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast

Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing

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