Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm

Zhao, Lu; Guang, Zeng; He, Ling‐Yun; Meng, Ya

doi:10.1007/s10614-018-9858-x

Cited by 7 publications

(9 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To compare the results of these algorithms, the error is measured by RMSE (root mean square error), MAPE (mean absolute percentage error), and the accuracy is thus assessed. The EV (error variance) is used to measure the stability of the predicted results [45,46]. The three statistical quantities are defined in Equations (12)-(14):…”

Section: Choice Of Oil Price Forecasting Modelmentioning

confidence: 99%

Forecasting Oil Price Using Web-based Sentiment Analysis

et al. 2019

Self Cite

View full text Add to dashboard Cite

International oil price forecasting is a complex and important issue in the research area of energy economy. In this paper, a new model based on web-based sentiment analysis is proposed. For the oil market, sentiment analysis is used to extract key information from web texts from the four perspectives of: compound, negative, neutral, and positive sentiment. These are constructed as feature and input into oil price forecasting models with oil price itself. Finally, we analyze the effect in various views and get some interesting discoveries. The results show that the root mean squared error can be reduced by about 0.2 and the error variance by 0.2, which means that the accuracy and stability are thereby improved. Furthermore, we find that different types of sentiments can all improve performance but by similar amounts. Last but not least, text with strong intensity can better support oil price forecasting than weaker text, for which the root mean squared error can be reduced by up to 0.5, and the number of the bad cases is reduced by 20%, indicating that text with strong intensity can correct the original oil price forecast. We believe that our research will play a strong supporting role in future research on using web information for oil price forecasting.

show abstract

Section: Choice Of Oil Price Forecasting Modelmentioning

confidence: 99%

Forecasting Oil Price Using Web-based Sentiment Analysis

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Between 2006 and 2017, there was a growing interest in applying GAs to methods such as support vector machines or leastsquares support vector regression (Guo et al 2012;Huang and Wang 2006;Yu et al 2016;Li and Ge 2013;Čeperić et al 2017). Despite the fact that some tools have outperformed GAbased approaches (which is discussed below), it is worth noticing that in the last 3 years, a few very promising GA methods have been offered, Namely, the multi-population genetic algorithm (Cheng et al 2018) and the empirical genetic algorithm (Zhao et al 2020), as well as successful ANFIS-based hybrids with GAs (Abd Herawati and Djunaidy 2020), have been introduced.…”

Section: Discussionmentioning

confidence: 99%

“…Fortunately, several further modifications of genetic algorithms can be introduced. The first example can be implementing the empirical distribution, as proposed by Zhao et al (2020). This is expected to radically diminish the risk of falling into the local optima.…”

Section: Discussionmentioning

confidence: 99%

“…However, GPM based on a genetic algorithm was significantly improved by Zhao et al (2020). Through the introduction of an empirical distribution into the specification of a genetic algorithm, and by the use of Markov chains, the resulting GPM model based on an empirical genetic algorithm (GPEGA) seemed to deal efficiently with oil price forecasting.…”

Section: Energy Commoditiesmentioning

confidence: 99%

“…Apparently, the mean absolute percentage error of GPEGA (which is proposed to avoid this danger) is reduced much more compared with the GPMGA than in the case of the estimations discussed above. Specifically, it is lowered by approximately 60% (Zhao et al 2020). Of course, it is hard to compare GPEGA with the hybrid proposed by Zhang et al (2014), as they are both compared with GPMGA based on different data.…”

Section: Energy Commoditiesmentioning

confidence: 99%

See 2 more Smart Citations

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

View full text Add to dashboard Cite

This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are efficient in dealing with non-stationary data. Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently. In parallel, researchers are also interested in constructing hybrid genetic algorithms (i.e., joining them with other econometric methods). Such an approach helps to reduce each of the individual method flaws and yields promising results. In this article, three groups of commodities are discussed: energy commodities, metals, and agricultural products. The advantages and disadvantages of genetic algorithms and their hybrids are presented, and further conclusions concerning their possible improvements and other future applications are discussed. This article fills a significant literature gap, focusing on particular financial and economic applications. In particular, it combines three important—yet not often jointly discussed—topics: genetic algorithms, their hybrids with other tools, and commodity price forecasting issues.

show abstract

Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach

et al. 2021

View full text Add to dashboard Cite

This paper provides an analysis of crude oil, diesel, and gasoline prices for the period from November 1, 2019 to December 31, 2020. We apply Log Periodic Power-Law Singularity (LPPLS) and Discrete Scale LPPLS bubble indicators to explore the dynamic bubbles of oil prices and predict their crash times. The results indicate that West Texas Light crude oil and North Sea Brent crude oil experienced a statistically significant negative financial bubble during the COVID-19 outbreak. In addition, gasoline and diesel prices are mainly driven by fundamentals. Our findings are expected to be useful to oil market investors, policymakers, and energy experts.

show abstract

Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm

Cited by 7 publications

References 45 publications

Forecasting Oil Price Using Web-based Sentiment Analysis

Forecasting Oil Price Using Web-based Sentiment Analysis

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach

Contact Info

Product

Resources

About