An Improved Prediction Model Combining Inverse Exponential Smoothing and Markov Chain

Niu, Tong; Zhang, Lin; Zhang, Bo; Yang, Bofan; Wei, Shengjun; Manfredi, Paolo

doi:10.1155/2020/6210616

Cited by 7 publications

(10 citation statements)

References 21 publications

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“…Also, we use the Diebold-Mariano (DM) test to test the pros and cons of the model from a statistical point of view [52]. At the same time, in order to further verify the accuracy of the CEEMD-combined model, the EMD-ARIMA model [53], the BP model [54], and the triple exponential smoothing model [55] were used for comparison and analysis. e di erent models and the accuracy indicators are shown in Table 6.…”

Section: Comparison and Analysis Of Combined Model Resultsmentioning

confidence: 99%

Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Hou

2022

Scientific Programming

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Accurate prediction of aircraft failure rate can improve flight safety and spare parts supply efficiency and effectively provide good maintenance and maintenance decisions and health management guidance. In order to achieve accurate prediction of non-linear and non-stationary aircraft failure rate, an aircraft failure rate prediction method based on the fusion of complementary ensemble empirical mode decomposition (CEEMD) and combined model is proposed. Firstly, the complementary set empirical mode is used to decompose the failure rate into multiple components with different frequencies, then the integrated moving average autoregressive model (ARIMA) model and grey Verhulst model are selected to predict different components, the entropy weight method is used to solve the coefficients of the combined model, and finally the prediction results of each prediction model are multiplied by their respective weight coefficients to obtain the final prediction results. The experiment was carried out by taking the actual case application of the failure rate data of the aircraft fuel control system as an example. Seven evaluation functions are used as evaluation criteria to evaluate the performance of the combined model. Experimental results show that the developed combined model is better than other models such as sum of squared error (SSE) and mean absolute error (MAE), which can significantly improve the prediction accuracy of aircraft failure rate. It is proved that the model can improve the accuracy and effectiveness of aircraft failure rate prediction. At the same time, the stability of the model has certain advantages over other models and has a good application prospect.

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Section: Comparison and Analysis Of Combined Model Resultsmentioning

confidence: 99%

Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Hou

2022

Scientific Programming

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“…e time-series analysis of a port cargo throughput refers to sequencing according to the generation time of a port cargo throughput data and analyzing the variation law of the port cargo throughput data. Based on this, the study provides reference for the construction of the following forecast model of a port cargo throughput [29]. Time-series analysis method is a series solution method, which combines numerical analysis, matrix theory, and many other analysis principles, and is widely used in many fields.…”

Section: Problem Statementmentioning

confidence: 99%

Algorithm Design of Port Cargo Throughput Forecast Based on the ES‐Markov Model

Sadr

2022

Discrete Dynamics in Nature and Society

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At present, the existing prediction algorithm of a port cargo throughput neglects the correction of the initial value of the cargo series data model, which leads to a large error in a port cargo throughput prediction. Therefore, a prediction algorithm of a port cargo throughput based on the ES-Markov model is designed. A decompose function is used to decompose the time series of a port cargo throughput, and the trend elements of a port cargo throughput are divided into long-term trend, seasonal trend, fluctuation trend, and irregular trend. In this study, the ES-Markov model is introduced, and the initial prediction is obtained by using the cubic exponential smoothing method, and the state transition matrix is obtained by the Markov principle. Based on the results of the time-series analysis and the ES-Markov model, the prediction algorithm of a port cargo throughput is designed. In the experimental design, the Elman neural network is used to construct an experimental sample data model. The monthly cargo throughput data of a certain port for eight months from May 2020 to December 2020 are collected and sorted according to the time series. The experimental results show that the prediction results of the proposed algorithm are closer to the actual value and the fluctuation of the prediction results is less than that of the reference.

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“…compare the prediction accuracy of the selected models [44,45]. The backward-selection method was used in the IW-BReg model to select the best fit in view of the covariates.…”

Section: Modelmentioning

confidence: 99%

“…The modeling performance was measured in terms of some criteria, such as AIC, D, D/df, and MSE [4]. We also used Theil's Inequality coefficient (TIC) to measure the prediction accuracy of the selected models [44,45]. To compare the residual for all models, we consider Pearson residuals to check the adequacy of the regression model fitted to the data [36,40].…”

Section: Modelmentioning

confidence: 99%

Inverted Weibull Regression Models and Their Applications

Al-Dawsari

2021

Stats

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In this paper, we propose the classical and Bayesian regression models for use in conjunction with the inverted Weibull (IW) distribution; there are the inverted Weibull Regression model (IW-Reg) and inverted Weibull Bayesian regression model (IW-BReg). In the proposed models, we suggest the logarithm and identity link functions, while in the Bayesian approach, we use a gamma prior and two loss functions, namely zero-one and modified general entropy (MGE) loss functions. To deal with the outliers in the proposed models, we apply Huber and Tukey’s bisquare (biweight) functions. In addition, we use the iteratively reweighted least squares (IRLS) algorithm to estimate Bayesian regression coefficients. Further, we compare IW-Reg and IW-BReg using some performance criteria, such as Akaike’s information criterion (AIC), deviance (D), and mean squared error (MSE). Finally, we apply the some real datasets collected from Saudi Arabia with the corresponding explanatory variables to the theoretical findings. The Bayesian approach shows better performance compare to the classical approach in terms of the considered performance criteria.

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An Improved Prediction Model Combining Inverse Exponential Smoothing and Markov Chain

Cited by 7 publications

References 21 publications

Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Algorithm Design of Port Cargo Throughput Forecast Based on the ES‐Markov Model

Inverted Weibull Regression Models and Their Applications

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