Forecasting Crude Oil Prices: a Deep Learning based Model

Chen, Yanhui; He, Kaijian; Tso, Kwok Fai

doi:10.1016/j.procs.2017.11.373

Cited by 102 publications

(36 citation statements)

References 17 publications

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“…As performance criteria, accuracy, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) were used. In [158], authors tried to predict WTI crude oil prices using several models including combinations of DBN, LSTM, Autoregressive Moving Average (ARMA) and RW. MSE was used as the performance criteria.…”

Section: Commodity Price Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

795

294

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Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.

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Section: Commodity Price Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

795

294

View full text Add to dashboard Cite

show abstract

“…Hybrid linear and non-linear techniques for energy demand forecasting in China and India are proposed by authors in [36]. To study the nonlinear complex nature of crude oil price movement Chen et al [37] proposed a deep learning based model and achieved improved forecasting accuracy. A GA and fast ensemble empirical mode decomposition (GA-FEEMD) for forecasting crude oil price time series data has been proposed by authors in [38].…”

Section: Related Studiesmentioning

confidence: 99%

A fireworks algorithm based Pi-Sigma neural network (FWA-PSNN) for modelling and forecasting chaotic crude oil price time series

Nayak¹

2018

EAI Endorsed Transactions on Energy Web

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Capturing the complex correlations among the data on the crude oil price time series is challenging, hence accurate prediction of it is difficult. Contrast to multilayer artificial neural network, Pi-Sigma neural network (PSNN) is characterized with stronger approximation ability, fast learning and higher fault tolerance. Fireworks algorithm (FWA) is a new metaheuristic motivated from the occurrence of fireworks explosion, characterized with fast convergence capability, parallelism and finds global optima. This article intends to achieve the synergetic effect of both on hybridizing them. It uses FWA for optimization of weight and bias of PSNN (FWA-PSNN) and overcomes the limitations of gradient based training. It has single hidden layer structure and trainable weight, hence fast and robust. The FWA-PSNN is evaluated on prediction of crude oil price time series. Extensive simulation results, performance analysis, and statistical significance test suggested the suitability of FWA-PSNN.

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“…In [14], various econometric models used to forecast crude oil prices are summarized and interpreted. In [13], a deep learning model is applied to crude oil prices and a hybrid crude oil price forecasting model is provided. In [12], oil producers' decisions in Cournot competitions are described through continuum dynamic mean field games.…”

Section: Introductionmentioning

confidence: 99%

Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning

2020

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A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN-S) model. Though this model is very efficient and analytically tractable, it suffers from the absence of long range dependence and many other issues. For this paper, the analysis is restricted to crude oil price dynamics. A simple way of improving the BN-S model with the implementation of various machine learning algorithms is proposed. This refined BN-S model is more efficient and has fewer parameters than other models which are used in practice as improvements of the BN-S model. The procedure and the model show the application of data science for extracting a "deterministic component" out of processes that are usually considered to be completely stochastic. Empirical applications validate the efficacy of the proposed model for long range dependence.

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Forecasting Crude Oil Prices: a Deep Learning based Model

Cited by 102 publications

References 17 publications

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

A fireworks algorithm based Pi-Sigma neural network (FWA-PSNN) for modelling and forecasting chaotic crude oil price time series

Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning

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