An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

scholarly journals Analyzing and Predicting Land Use and Land Cover Changes in New Jersey Using Multi-Layer Perceptron–Markov Chain Model

Earth ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 845-870
Author(s):  
Kikombo Ilunga Ngoy ◽  
Feng Qi ◽  
Daniela J. Shebitz
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
New Jersey ◽  
Land Cover ◽  
Markov Chain Model ◽  
Mixed Forest ◽  
The United States ◽  
Chain Model ◽  
Multi Layer Perceptron ◽  
Lulc Changes

This study analyzed the changes of land use and land cover (LULC) in New Jersey in the United States from 2007 to 2012. The goal was to identify the driving factors of these changes and to project the five-year trend to 2100. LULC data was obtained from the New Jersey Department of Environmental Protection. The original 86 classes were reclassified to 11 classes. Data analysis and projection were performed using TerrSet 2020. Results from 2007 to 2012 showed that the rate of LULC changes was relatively small. Most changes happened to brush/grasslands, mixed forest lands, farmlands and urban/developed lands. Urban/developed lands and the mixed-forest cover gained while farmlands lost. Using a multi-layer perceptron–Markov chain (MLP–MC) model, we projected the 2015 LULC and validated by actual data to produce a 2100 LULC. Changes from 2012 to 2100 showed that urban/developed lands, as well as brush/grasslands, would continue to gain, while farmlands would lose, although the projected landscape texture would likely be identical to the 2012 landscape. Human and natural factors were discussed. It was concluded that the MLP–MC model could be a useful model to predict short-term LULC change. Unexpected factors are likely to interfere in a long-term projection.

scholarly journals AGRICULTURAL LAND CHANGE DETECTING AND FORECASTING USING COMBINATION OF FEEDFORWARD MULTILAYER NEURAL NETWORK, CELLULAR AUTOMATA AND MARKOV CHAIN MODELS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 153-158
Author(s):  
A. Babaeian Diva ◽  
B. Bigdeli ◽  
P. Pahlavani
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Cellular Automata ◽  
Agricultural Land ◽  
Markov Chain Model ◽  
Support Vector ◽  
Chain Model ◽  
Landsat Images ◽  
The Neural Network ◽  
Landsat Satellite

Abstract. This paper proposed a methodology for finding changes in agricultural land of Tehran during past years and simulating these changes for future years. The proposed method utilized the spatial GIS-based techniques and Landsat satellite imagery to predict agricultural land map for the future of Tehran. Therefore, a method for finding and predicting changes based on combining the feedforward multilayer perceptron neural network (MLP), cellular automata (CA), and Markov chain model were applied. In this regard, the Landsat images of 2002, 2008, and 2014 were classified by a binary support vector machine classifier into two classes of agricultural and non-agricultural. Then, the potential transition maps were generated by the neural network MLP and extensible areas were obtained by the Markov chain model. Finally, the results of these two steps were combined with the MOLA method and the 2020 and 2025 agricultural maps were predicted. The proposed method obtained the Kappa factor of 89.92% that indicates the high ability of the neural network and the CA–Markov for finding the changes and prediction in the city of Tehran.

scholarly journals Hubungan Panjang Seri Data Historik Terhadap Kualitas Data Hasil Prediksi Pada Penerapan Model Rantai Markov Untuk Peramalan Aliran Sungai

2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Sulianto .
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
River Flow ◽  
Historical Data ◽  
Markov Chain Model ◽  
Low Flow ◽  
Data Series ◽  
Chain Model ◽  
Flow Data ◽  
Data Limitations

Markov Chain Model is a stochastic model for forecasting the river flow which in his analysis always involves a long series of historical data. In most studies the method is still highly theoretical and not fully applicable significantly due to the limited data in the field.This study is an attempt to optimize the application of Markov Chain Model for its functionality extensively to extrapolate data streams. The scope of this research is basically conducted a study on the relationship between the length of the historical flow data series with data quality prediction results. By knowing these characteristics, the error correction of analysis results can be expected due to data limitations, so that the Markov Chain Model can be widely applied to optimization of waterworks operations.Results for the Konto River and River showed that the prediction of flow Kwayangan next year with Markov chain models tend to give better results than the results of forecasting by conventional methods are widely applied. Markov model is good enough to predict the river flow has low flow fluctuations, but for a river flow fluctuated sharply less than satisfactory results. The length of data series ranges from 15 to 20 of the optimal inputs to produce a minimum error rate prediction. Accuracy of prediction result is not determined by the length of the input data series, but is determined by the nature of statistical data. Value of lag-1 correlation coefficient are large and small skewness coefficient of the historical data tends to give a satisfactory prediction results.Key words: river flow, data, prediktion, markov model.

scholarly journals Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Siqi Hua
Keyword(s):  
Neural Network ◽  
Economic Development ◽  
Gross Domestic Product ◽  
Linear Part ◽  
Back Propagation ◽  
Weighted Average ◽  
Domestic Product ◽  
Fusion Model ◽  
The Government ◽  
Predicted Values

GDP (gross domestic product) is a key indicator for assessing a country’s or region’s macroeconomic situation, as well as a foundation for the government to develop economic development strategies and macroeconomic policies. Currently, the majority of methods for forecasting GDP are linear methods, which only take into account the linear factors that affect GDP. GDP (gross domestic product) is widely regarded as the most accurate indicator of a country’s economic health. GDP not only reflects a country’s economic development over time but can also reflect its national strength and wealth. As a result, the GDP trend forecast partially reflects China’s transformation and future development. The time series ARIMA (Autoregressive Integrated Moving Average) model and the BPNN (BP neural network) model are combined in this article to create the ARIMA-BPNN fusion prediction model. The predicted values of the two models were then weighted averaged to obtain the predicted values of the linear part of the improved fusion model. To get the predicted values of the improved fusion model, we weighted average the residual parts of the two models, predict the nonlinear residual with BPNN, and add the predicted values of the two parts. It is applied to the actual GDP forecast in H province from 2019 to 2022, and the actual forecast verifies the effectiveness of the fusion forecast model in the actual forecast.

scholarly journals Cointegration Analysis and Forecasting of the Export Function of Bangladesh Using the Error Correction Model

2020 ◽  
Author(s):  
K M Saemon Islam ◽  
Gautam Kumar Biswas
Keyword(s):  
United States ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Correction Term ◽  
The United States ◽  
Error Correction Model ◽  
Cointegration Analysis ◽  
Domestic Product ◽  
Correction Model ◽  
The Relationship

Abstract In this paper, we examined the relationship between the growth of the Gross Domestic Product of the United States, the export value index, and the export of Bangladesh over 37 years between 1980 and 2016. The results of our preliminary tests showed that there was indeed a long-run relationship between these variables. Based on our preliminary analysis, we employed an error-correction model to identify the relationship between the variables. The error-correction term with the expected negative sign was statistically significant, and it confirmed that in the case of disequilibrium, the convergence towards the equilibrium happened in the subsequent periods. Additionally, the econometric estimates exhibited that the two-period lagged values of the growth in export of Bangladesh and the growth of the Gross Domestic Product of the United States were also statistically significant.JEL Classification: C22, C5, F41

Application of grey systems in predicting the degree of cotton spider mite infestations

2017 ◽  
Vol 7 (3) ◽  
pp. 353-364 ◽  
Author(s):  
Shouhui Wang ◽  
Jianguo Dai ◽  
Qingzhan Zhao ◽  
Meina Cui
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Bp Neural Network ◽  
Spider Mite ◽  
Markov Chain Model ◽  
Grey Model ◽  
Chain Model ◽  
Content Type ◽  
Bp Neural Network Model ◽  
Mite Infestations

Purpose Many factors affect the emergence and development of crop diseases and insect pests. Traditional methods for investigating this subject are often difficult to employ and produce limited data with considerable uncertainty. The purpose of this paper is to predict the annual degree of cotton spider mite infestations by employing grey theory. Design/methodology/approach The authors established a GM(1,1) model to forecast mite infestation degree based on the analysis of historical data. To improve the prediction accuracy, the authors modified the grey model using Markov chain and BP neural network analyses. The prediction accuracy of the GM(1,1), Grey-Markov chain, and Grey-BP neural network models was 84.31, 94.76, and 96.84 per cent, respectively. Findings Compared with the single grey forecast model, both the Grey-Markov chain model and the Grey-BP neural network model had higher forecast accuracy, and the accuracy of the latter was highest. The improved grey model can be used to predict the degree of cotton spider mite infestations with high accuracy and overcomes the shortcomings of traditional forecasting methods. Practical implications The two new models were used to estimate mite infestation degree in 2015 and 2016. The Grey-Markov chain model yielded respective values of 1.27 and 1.15, whereas the Grey-BP neural network model yielded values 1.4 and 1.68; the actual values were 1.5 and 1.8. Originality/value The improved grey model can be used for medium- and long-term predictions of the occurrence of cotton spider mites and overcomes problems caused by data singularity and fluctuation. This research method can provide a reference for the prediction of similar diseases.

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