The improved grey model based on particle swarm optimization algorithm for time series prediction

Li, Kewen; Liu, Lu; Zhai, Jiannan; Khoshgoftaar, Taghi M.; Li, Timing

doi:10.1016/j.engappai.2016.07.005

Cited by 69 publications

(28 citation statements)

References 8 publications

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“…However, the practical data sequence often exhibits the characteristic of approximately inhomogeneous exponential growth [48,49]. In addition, the calculation of the background value of GM(1,1) model leads to bias and affects the prediction performance [6,53,54]. Therefore, in this paper, we optimized the GM(1,1) model by combining data transformation for the original data sequence and combination interpolation optimization of the background value, namely DCOGM(1,1).…”

Section: Discussionmentioning

confidence: 99%

“…Chung [6] applied an improved GM(1,1) model named the NNGM(1,1), which is a neural-network-based GM(1,1) model, to solve the troublesome problem of the background value estimation by automatically determining the grey developed coefficient a and the grey controlled variable b. Zhao and Guo [44] proposed the Rolling-ALO-GM(1,1) model with improved prediction accuracy to forecast the annual electricity consumption in China. Li et al [53] proposed an improved grey model (PGM(1,1) model) based on particle swarm optimization algorithm, and achieved better prediction performance. Hsu [54] brought up an improved transformed grey model based on a genetic algorithm (ITGM(1,1)), which exhibited better in-sample and out-of-sample forecasting performance.…”

Section: Methodology Of the Combined Optimized Gm(11) Modelmentioning

confidence: 99%

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Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Zhang

2018

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Prediction of electricity consumption plays critical roles in the economy. Accurate electricity consumption forecasting is essential for policy makers to formulate electricity supply policies. However, limited data and variables generally cannot provide sufficient information to gain satisfactory prediction accuracy. To address this problem, we propose a novel improved grey forecasting model, which combines data transformation for the original data sequence and combination interpolation optimization of the background value of the GM(1,1) model, and is therefore named DCOGM(1,1). To evaluate the simulation and prediction performance of DCOGM(1,1), two case studies are carried out. In addition, the results show that DCOGM(1,1) outperforms most existing improved grey models in terms of forecasting accuracy. Finally, DCOGM(1,1) is employed to predict the total electricity consumption of Shanghai City in China from 2017 to 2021. In addition, the results suggest that DCOGM(1,1) performs well compared with the traditional GM(1,1) model and other grey modification models in this context and Shanghai's electricity consumption will increase stably in the following five years. In summary, DCOGM(1,1) proposed in our study has competent exploration and exploitation ability, and could be utilized as an effective and promising tool for short-term planning for other forecasting issues with limited source data as well.The forecast accuracy of electricity consumption is affected by many factors, such as economic development [8], population [9], power facilities [10] and climate factors [11], and thus the data sequence of electricity consumption often presents to be highly nonlinear, stochastic, time-changeable,

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Section: Discussionmentioning

confidence: 99%

Section: Methodology Of the Combined Optimized Gm(11) Modelmentioning

confidence: 99%

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Zhang

2018

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“…, n) into the structure of background values in GMC (1, n), namely, the background values for the system behavior sequence Z (1) 1 (rp + t) � λ 1 X (1) 1 (rp + t) + (1 − λ 1 )X (1) 1 (rp + t − 1) and the background values for the relevant sequences Z (1) i (t) � λ i X (1) i (t) + (1 − λ i )X (1) i (t − 1), i � 2,3,.. .. Subsequently, for the purpose of further enhancing precision, various heuristic intelligent techniques are employed to determine the background value coefficient, including Particle Swarm Optimization (PSO) [24][25][26], Genetic Algorithm (GA) [27,28], Ant Lion Optimizer (ALO) [29], and Ant Colony Algorithm (ACA) [30].…”

Section: Introductionmentioning

confidence: 99%

A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications

Ding

2020

Complexity

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Accurate estimations can provide a solid basis for decision-making and policy-making that have experienced some kind of complication and uncertainty. Accordingly, a multivariable grey convolution model (GMC (1, n)) having correct solutions is put forward to deal with such complicated and uncertain issues, instead of the incorrect multivariable grey model (GM (1, n)). However, the conventional approach to computing background values of the GMC (1, n) model is inaccurate, and this model's forecasting accuracy cannot be expected. ereby, the drawback analysis of the GMC (1, n) model is conducted with mathematical reasoning, which can explain why this model is inaccurate in some applications. In order to eliminate the drawbacks, a new optimized GMC (1, n), shorted for OGMC (1, n), is proposed, whose background values are calculated based on Simpson' rule that is able to efficiently approximate the integration of a function. Furthermore, its extended version that uses the Gaussian rule to discretize the convolution integral, abbreviated as OGMC G (1, n), is proposed to further enhance the model's forecasting ability. In general, these two optimized models have such advantages as simplified structure, consistent forecasting performance, and satisfactory efficiency. ree empirical studies are carried out for verifying the above advantages of the optimized model, compared with the conventional GMC (1, n), GMC G (1, n), GM (1, n), and DGM (1, n) models. Results show that the new background values can effectively be calculated based on Simpson's rule, and the optimized models significantly outperform other competing models in most cases.

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“…It requires only four recent samples to derive reliable and acceptable prediction accuracy [5], and has been widely applied to various decision problems involving management, economics, and engineering [2][3][4][11][12][13][14][15][16]. To better improve the prediction performance of the original GM(1,1) model, several versions combining with computational intelligence have been proposed, such as models with self-adaptive intelligence [17], neural-network-based grey prediction for electricity consumption prediction [18,19], PGM(1,1) using particle swarm optimization to determine the development coefficient [20], GM(1,1) models with online sequential extreme learning machine [21], an optimized nonlinear grey Bernoulli model [22], an adaptive GM(1,1) for electricity consumption [3], and grey wave forecasting through qualified contour sequences [23]. Literally, the combination of grey prediction and neural networks can better represent system dynamics with uncertainty and nonlinearity [21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Grey Prediction Model Combining Markov Chain with Functional-Link Net and Its Application to Foreign Tourist Forecasting

Jiang

Chiu

et al. 2017

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Grey prediction models for time series have been widely applied to demand forecasting because only limited data are required for them to build a time series model without any statistical assumptions. Previous studies have demonstrated that the combination of grey prediction with neural networks helps grey prediction perform better. Some methods have been presented to improve the prediction accuracy of the popular GM(1,1) model by using the Markov chain to estimate the residual needed to modify a predicted value. Compared to the previous Grey-Markov models, this study contributes to apply the functional-link net to estimate the degree to which a predicted value obtained from the GM(1,1) model can be adjusted. Furthermore, the troublesome number of states and their bounds that are not easily specified in Markov chain have been determined by a genetic algorithm. To verify prediction performance, the proposed grey prediction model was applied to an important grey system problem-foreign tourist forecasting. Experimental results show that the proposed model provides satisfactory results compared to the other Grey-Markov models considered.

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The improved grey model based on particle swarm optimization algorithm for time series prediction

Cited by 69 publications

References 8 publications

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications

A Novel Grey Prediction Model Combining Markov Chain with Functional-Link Net and Its Application to Foreign Tourist Forecasting

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