Combined Forecast Model of Refined Oil Demand Based on Grey Theory

Zhang, Qimin; Chen, Ning; Zhao, Junchao

doi:10.1109/iptc.2010.56

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Since then, much effort has been devoted to developing and improving the parallel hybrid models to enhance time series forecasting performance. Zhang et al (2012) have proposed a parallel hybrid model based on the Gray Theory for Oil demand forecasting. Barassi and Zhao (2017) have developed a parallel hybrid model for shortterm forecasting of the demand for energy produced from five different sources in the UK averaging from a set of six univariate and multivariate models comprising ARMA, Holt-Winters, Non-Linear Autoregressive Neural Networks (NLANN), Vector Autoregressive (VAR), Bayesian VAR and Factor Augmented VAR (FAVAR).…”

Section: Introductionmentioning

confidence: 99%

A parallel-series hybridization of seasonal intelligent based statistical model for demand forecasting

Bahrami

Khashei

Amindoust

2021

JM2

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Purpose The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting. Design/methodology/approach The main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components. Findings Empirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed. Originality/value To the best of the authors’ knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed.

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Section: Introductionmentioning

confidence: 99%

A parallel-series hybridization of seasonal intelligent based statistical model for demand forecasting

Bahrami

Khashei

Amindoust

2021

JM2

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“…In the traditional statistical model, it mainly includes the autoregressive integrated moving average model (ARIMA) [4,5], random walk (RW) model [6], grey forecast model [7], error correction model (ECM), Bayesian model average (BMA) [8], the panel data regression [9], and so on. The above models are often used to predict time series.…”

Section: Introductionmentioning

confidence: 99%

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

2021

Energies

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In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component prediction and ensemble output. In the data trait test and component trait analysis, the original time series and each decomposed component are thoroughly analyzed to explore hidden data traits. According to these results, decomposition models and prediction models are selected to complete the original time series data decomposition and decomposed component prediction. In the ensemble output, the ensemble method corresponding to the decomposition method is used for final aggregation. In particular, this methodology introduces the rolling mechanism to solve the misuse of future information problem. In order to verify the effectiveness of the model, the quarterly gasoline consumption data from four provinces in China are used. The experimental results show that the proposed model is significantly better than the single prediction models and decomposition-ensemble models without the rolling mechanism. It can be seen that the decomposition-ensemble model with data-trait-driven modeling ideas and rolling decomposition and prediction mechanism possesses the superiority and robustness in terms of the evaluation criteria of horizontal and directional prediction.

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Research of Oil Product Secondary Distribution Optimization Based on Collaborative Distribution

Wang

Zhan

Zhang

2015

Procedia Computer Science

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Combined Forecast Model of Refined Oil Demand Based on Grey Theory

Cited by 3 publications

References 3 publications

A parallel-series hybridization of seasonal intelligent based statistical model for demand forecasting

A parallel-series hybridization of seasonal intelligent based statistical model for demand forecasting

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

Research of Oil Product Secondary Distribution Optimization Based on Collaborative Distribution

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