Grey double exponential smoothing model and its application on pig price forecasting in China

Wu, Lifeng; Liu, Sifeng; Yang, Yingjie

doi:10.1016/j.asoc.2015.09.054

Cited by 104 publications

(64 citation statements)

References 17 publications

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“…Peneliti yang sama juga menggunakan Adaptive Rate Response Single Exponential Smoothing untuk meramalkan permintaan barang guna pengendalian persediaan barang pada periodic review policy [4]. Double Exponential Smoothing juga digunakan oleh Wu untuk meramalkan harga babi di China [5].…”

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Sistem Peramalan Harga Emas Antam Menggunakan Double Exponential Smoothing

Andriyanto¹

2017

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Sistem Peramalan Harga Emas Antam Menggunakan Double Exponential Smoothing

Andriyanto¹

2017

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“…However, the accumulation of integer order was not suitable for some data, as modeling, and the effects of simulation and prediction were poor. Nevertheless, the current fraction grey model which used factional order accumulation [24][25][26][27] has the important significance for improving the performance of grey model. Therefore, Wu et al [28] defined the actual data with fractional order accumulation and defined the NDGM model with fractional order accumulation (NDGM 푝/푞 ).…”

Section: Introductionmentioning

confidence: 99%

Research on the Stability of NDGM Model with the Fractional Order Accumulation and Its Optimization

Duan

Shao

Xiao

et al. 2017

Mathematical Problems in Engineering

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The grey forecasting model has been successfully applied in numerous fields since it was proposed. The nonhomogeneous discrete grey model (NDGM) was approximately constructed based on the nonhomogeneous index trend; it increased the applicability of discrete grey model. However, the NDGM required accurate data and better effect when the original data did not meet the conditions and fitting and prediction errors were larger. For this, the NDGM with the fractional order accumulating operator (abbreviated as NDGM 푝/푞 ) has higher performance. In this paper, the matrix perturbation bound of the parameters was used to analyze the stability of NDGM 푝/푞 and the NDGM 푝/푞 can decrease the disturbance bound. Subsequently, the parameter estimation method of NDGM 푝/푞 was studied and the Particle Swarm Optimization algorithm was employed to optimize the order number of NDGM 푝/푞 and some steps were provided. In addition, the results of two practical examples demonstrated that the perturbation of NDGM 푝/푞 was smaller than that of NDGM and provided remarkable predication performance compared with the traditional NDGM model and DGM model.

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“…Since its introduction, the gray prediction model has been widely studied and continuously developed and optimized [27][28][29][30]. GM (1,1), as the core gray prediction model, has also been improved [25,31,32] and has been widely used in various fields. However, in practical applications and the theoretical research process, GM(1, 1) is not fully suitable for fitting homogenous exponential series.…”

Section: Introductionmentioning

confidence: 99%

“…After 30 years of development, a framework of system analysis and evaluation [22], model prediction [23][24][25], and decision control [26] has been established as the main technical system. The gray prediction model is the core component of gray system theory.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model

2017

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The traffic-flow system has basic dynamic characteristics. This feature provides a theoretical basis for constructing a reasonable and effective model for the traffic-flow system. The research on short-term traffic-flow forecasting is of wide interest. Its results can be applied directly to advanced traffic information systems and traffic management, providing real-time and effective traffic information. According to the dynamic characteristics of traffic-flow data, this paper extends the mechanical properties, such as distance, acceleration, force combination, and decomposition, to the traffic-flow data vector. According to the mechanical properties of the data, this paper proposes four new models of structural parameters and component parameters, inertia nonhomogenous discrete gray models (referred to as INDGM), and analyzes the important properties of the model. This model examines the construction of the inertia nonhomogenous discrete gray model from the mechanical properties of the data, explaining the classic NDGM modeling mechanism in the meantime. Finally, this paper analyzes the traffic-flow data of Whitemud Drive in Canada and studies the relationship between the inertia model and the traffic-flow state according to the data analysis of the traffic-flow state. A simulation accuracy and prediction accuracy of up to 0.0248 and 0.0273, respectively, are obtained.

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Grey double exponential smoothing model and its application on pig price forecasting in China

Cited by 104 publications

References 17 publications

Sistem Peramalan Harga Emas Antam Menggunakan Double Exponential Smoothing

Sistem Peramalan Harga Emas Antam Menggunakan Double Exponential Smoothing

Research on the Stability of NDGM Model with the Fractional Order Accumulation and Its Optimization

Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model

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