Tourism Flows Prediction based on an Improved Grey GM(1,1) Model

Liu, Xiangyun; Peng, Hongqin; Bai, Yun; Zhu, Yujun; Liao, Lueling

doi:10.1016/j.sbspro.2014.07.256

Cited by 56 publications

(31 citation statements)

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“…(17) A few studies have enhanced the predictive accuracy of multiple aspects, such as the background and original values. (18) In this study, we use ARGM (1, 1) based on the RGM, (19) which uses the moving-average windows (MAW) method to deal with the raw data points of the FOG x (0) (i), (i = 1, 2, ..., n). First, it uses an accumulation process to deal with the original data, and then uses the MAW method to deal with the entire number sequence.…”

Section: Design Of the Argm (1 1) Modelmentioning

confidence: 99%

A Novel Method Based on a High-Dynamic Hybrid Forecasting Model for Fiber Optic Gyroscope Drift

2017

Sensors and Materials

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The drift of a fiber optic gyroscope (FOG) has a significant impact on the precision of an inertial navigation system (INS). In order to predict the FOG drift more efficiently, we have developed a method of reducing the drift using a hybrid-forecasting model. In the proposed model, the systematic and random parts of the FOG drift data are decomposed using the empirical mode decomposition (EMD) model. Then the systematic part is predicted by employing the adaptive residual grey model [ARGM (1, 1)], and the random part is predicted by the improved autoregressive moving-average (IARMA) model. The final prediction results are the superimposition of the respective prediction using the EMD reconstruction model. The experimental results show that the gyroscope drift can be forecast precisely and can provide a basis for gyroscope performance analysis and fault prediction. At the same time, it can be concluded that the hybrid modeling has a higher forecasting precision than the single forecasting method.

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Section: Design Of the Argm (1 1) Modelmentioning

confidence: 99%

A Novel Method Based on a High-Dynamic Hybrid Forecasting Model for Fiber Optic Gyroscope Drift

2017

Sensors and Materials

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“…Liu et al [32] developed an optimization model by improving the grey model and enhancing prediction accuracy after referring to initial and background values. Xie and Liu [33] proposed a novel discrete grey forecasting model, termed the DGM model, which increased the tendency catching ability of the model.…”

Section: Problems With Predicting Productionmentioning

confidence: 99%

Forecasting China’s Annual Biofuel Production Using an Improved Grey Model

et al. 2015

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Biofuel production in China suffers from many uncertainties due to concerns about the government's support policy and supply of biofuel raw material. Predicting biofuel production is critical to the development of this energy industry. Depending on the biofuel's characteristics, we improve the prediction precision of the conventional prediction method by creating a dynamic fuzzy grey-Markov prediction model. Our model divides random time series decomposition into a change trend sequence and a fluctuation sequence. It comprises two improvements. We overcome the problem of considering the status of future time from a static angle in the traditional grey model by using the grey equal dimension new information and equal dimension increasing models to create a dynamic grey prediction model. To resolve the influence of random fluctuation data and weak anti-interference ability in the Markov chain model, we improve the traditional grey-Markov model with classification of states using the fuzzy set theory. Finally, we use real data to test the dynamic fuzzy prediction model. The results prove that the model can effectively improve the accuracy of forecast data and can be applied to predict biofuel production. However, there are still some defects in our model. The modeling approach used here predicts biofuel production levels based upon past production levels dictated by economics, governmental policies, and technological developments but none of which can be forecast accurately based upon past events.

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“…The ARMA model is shown in Table 1. ARMA (2,11) ARMA (4,14) ARMA (2,20) AR (5) The eight ARMA models are used to forecast. Then the forecasting results are restructured and the final prediction value will be got.…”

Section: Figure 3 Components Of Imf Of Originaldatamentioning

confidence: 99%

“…And there are mainly three aspects on studying the tourism demand forecasting. Firstly, methods based on the trend of historical data, including regression analysis method [1][2], time series method [3][4][5][6][7][8][9], grey forecasting method [10][11][12][13]. Secondly, forecasting the tourism demand based on the tourist market investigation and the analysis of purchasing power [14][15].…”

Section: Introductionmentioning

confidence: 99%