Based on SARIMA-BP hybrid model and SSVM model of international crude oil price prediction research

Luo, Hui; Liu, Xing; Wang, Shuang

doi:10.21914/anziamj.v58i0.10995

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Similarly, Shuyu Li et al [18] in 2017 compared ARIMA, GM and ARIMA-GM model with each other, in which it was concluded that ARIMA-GM shows high accuracy. Hua Luo et al [1] in 2017 proposed two hybrid models SARIMA-BP and SSVM. After comparing the two models, SSVM performed better.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this paper the time series models used for electric load forecasting are SARIMA and ARIMA. Time series analysis is observing the time sequence and then finding its change in trend, forecasting its future [20].Time series analysis technique becomes a very efficient method when we can't find the important factors that leads to data conversions, from many other factors [1].…”

Section: Introductionmentioning

confidence: 99%

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SARIMA Modelling for Forecasting the Electricity Consumption of a Health Care Building

Kaur*¹,

Ahuja²

2019

IJITEE

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Healthcare buildings have an immense demand for electricity, because of which they exhibit distinctive ability for forecasting of electricity consumption. In this work, ability for forecasting electricity consumption of a large healthcare building was researched. Both the techniques change nonstationary data into stationary data to make an effective and simple data representation and removing of noise subspaces. The comparison of experimental results is done among the SARIMA and ARIMA models. Analysis of the results concludes that performance of SARIMA is better when compared to ARIMA model. The analysis of data from 11 years in the hospital demonstrates that these dynamic models are sufficiently adaptable to forecast the electricity consumption at required accuracy levels.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SARIMA Modelling for Forecasting the Electricity Consumption of a Health Care Building

Kaur*¹,

Ahuja²

2019

IJITEE

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“…Thus, many fast optimization algorithms can be used to solve it, which greatly reduces the PLOS ONE computational complexity. SVM has strict convexity and infinitely differentiable mathematical characteristics, and introduced efficient unconstrained optimization problems, which attracted many scholars to study SSVM models from different perspectives [23][24][25][26][27][28][29], Some proposed different smoothing functions [23][24][25][26], while others extended them to the prediction field [16,17]. Other applications of SVM and SSVM are introduced in [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Construction of a new smooth support vector machine model and its application in heart disease diagnosis

Wang

Sun

2023

PLoS ONE

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Support vector machine (SVM) is a new machine learning method developed from statistical learning theory. Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of fast optimization algorithms can’t be used to find the solution. Firstly, to overcome the non-smooth property of this model, a new padé33 approximation smooth function is constructed by rational approximation method, and a new smooth support vector machine model (SSVM) is established based on the smooth function. Then, by analyzing the performance of the smooth function, we find that the smooth precision is significantly higher than existing smooth functions. Moreover, theoretical and rigorous mathematical analyses are given to prove the convergence of the new model. Finally, it is applied to the heart disease diagnosis. The results show that the Padé33-SSVM model has better classification capability than existing SSVMs.

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“…Ahmad, Abdul [22] used the SARIMA model to predict the crude palm oil and kernel palm production between June 2011 and May 2011 with the study results also showing that SARIMA is the most fitted model compared to other methods. Furthermore, Luo et al [23] deployed SARIMA with a back propagation neural network (SARIMA-BP) hybrid model to forecast the international crude oil price from January 2002 to April 2006.…”

Section: Introductionmentioning

confidence: 99%

Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe

Lee

Nguyen

et al. 2022

Energies

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Crude oil price volatility impacts the global economy in general, as well as the economies of Europe and the United States in particular; it is supremely difficult to describe its tendency precisely, hence it leads to a forecasting methodology. This study aims to use the autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) approaches to cope with this problem in the United States and Europe. The data was gathered from the U.S. Energy Information Administration and federal research economic data (FRED) from January 2017 to September 2021. Simultaneously, values from January 2017 to March 2021, with 51 observations accounting for 90% of the total samples, were employed for the training phase, and the rest were used for the testing phase. The forecast result also indicated that the root mean square error (RMSE) and mean absolute percentage error (MAPE) values, applied by ARIMA models in Europe and the United States, have higher accurate indicators than SARIMA models. As a result, the ARIMA model achieved the best accuracy in both Europe and the USA, with MAPEEurope−ARIMA = 0.05, and MAPEUSA−ARIMA=0.05. Based on these accuracy parameters, the forecasting models appear incredibly reliable; similarly, the study results might assist governing bodies in making significant decisions, thereby accelerating socio-economic development in the world’s two largest economies.

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Based on SARIMA-BP hybrid model and SSVM model of international crude oil price prediction research

Cited by 5 publications

References 15 publications

SARIMA Modelling for Forecasting the Electricity Consumption of a Health Care Building

SARIMA Modelling for Forecasting the Electricity Consumption of a Health Care Building

Construction of a new smooth support vector machine model and its application in heart disease diagnosis

Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe

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