Ensemble Incremental Random Vector Functional Link Network for Short-term Crude Oil Price Forecasting

2018 ◽  
Author(s):  
Xueheng Qiu ◽  
P. N. Suganthan ◽  
A. J. Gehan Amaratunga
Keyword(s):  
Crude Oil ◽  
Random Vector ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Short Term ◽  
Functional Link

scholarly journals Crude Oil Price Forecasting Using Long Short-Term Memory

2021 ◽  
Vol 7 (2) ◽  
pp. 286
Author(s):  
Muhamad Fariz Maulana ◽  
Siti Sa’adah ◽  
Prasti Eko Yunanto
Keyword(s):  
Crude Oil ◽  
Short Term Memory ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Impact of crude oil price on the socio-political environment of global countries

2019 ◽  
Vol 118 (3) ◽  
pp. 110-122
Author(s):  
Johnson Clement Madathil ◽  
Velmurugan P. S
Keyword(s):  
Crude Oil ◽  
Descriptive Analysis ◽  
Oil Price ◽  
Policy Framework ◽  
Political Environment ◽  
Policy Reforms ◽  
Crude Oil Price ◽  
Short Term ◽  
Political Impact ◽  
New Energy

Crude oil is known to have an impact on people’s life of both producers and consumers of crude oil countries. A producer country’s socio-political impact will be different from a consumer country’s socio-political impact. This paper aims to show that crude oil price has a socio-political impact on global countries through descriptive analysis. The study found that there were similarities in the movement of crude oil price and change in GDP of both India and United States and further Russia and Venezuela have had crude oil impact on their respective GDP’s, which has made them take policy reforms. The paper identifies changes in the policy framework due to influence of crude oil price and eventual changes in existing socio-political environment. Taking oil producing countries such as Russia and Venezuela as examples, this paper suggests that policy reforms are the key to having a stable socio-political environment. Russia shows us that having a flexible monetary policy can keep the budget dependence on crude oil reduced in the short term. On the other hand, for oil consuming countries, having a stable supply and moving to new energy sources is the key to tackle the influence of crude oil price on the socio-political environment of global countries.

scholarly journals Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models

2015 ◽  
Vol 7 (5) ◽  
pp. 127-136 ◽  
Author(s):  
Yumurtaci Aydo mu Hacer ◽  
Ekinci Aykut ◽  
Erdal Halil ◽  
Erdal Hamit
Keyword(s):  
Crude Oil ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Forecasting Accuracy ◽  
Ensemble Models ◽  
Bagging Ensemble

scholarly journals A Research on Markov Model and Hidden Markov Model in Crude Oil Price Forecasting for PETRONAS Malaysia

2019 ◽  
Vol 8 (12S2) ◽  
pp. 121-125 ◽  
Keyword(s):  
Markov Model ◽  
Crude Oil ◽  
Hidden Markov Model ◽  
Structural Equation ◽  
Hidden Markov ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Accurate Forecast

Crude oil price forecasting is an essential component of sustainable development of many countries as crude oil is an unavoidable product that exists on earth. In this paper, a model based on a hidden Markov model and Markov model for crude oil price forecasting was developed, and their relative performance was compared. Path analysis of Structural Equation Modelling was employed to model the effects of forecasted prices and the actual crude oil price to get the most accurate forecast. The key variables used to develop the models were monthly crude oil prices s from PETRONAS Malaysia. It was found that the hidden Markov model was more accurate than the Markov model in forecasting the crude oil price. The findings of this study show that the hidden Markov model is a potentially promising method of crude oil price forecasting that merit further study.

scholarly journals Oil Price Forecasting Using a Time-Varying Approach

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1403
Author(s):  
Lu-Tao Zhao ◽  
Shun-Gang Wang ◽  
Zhi-Gang Zhang
Keyword(s):  
Crude Oil ◽  
Time Window ◽  
Oil Prices ◽  
Oil Price ◽  
Price Forecasting ◽  
Time Varying ◽  
Crude Oil Price ◽  
Trend Prediction ◽  
Oil Market ◽  
Polynomial Decomposition

The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

scholarly journals Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Štifanić ◽  
Jelena Musulin ◽  
Adrijana Miočević ◽  
Sandi Baressi Šegota ◽  
Roman Šubić ◽  
...  
Keyword(s):  
Crude Oil ◽  
Stock Prices ◽  
Short Term Memory ◽  
Oil Price ◽  
Stationary Wavelet Transform ◽  
Crude Oil Price ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Impact

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

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