A Streaming Data Prediction Method Based on Evolving Bayesian Network

2017 ◽  
pp. 294-302
Author(s):  
Yongheng Wang ◽  
Guidan Chen ◽  
Zengwang Wang
Keyword(s):  
Bayesian Network ◽  
Prediction Method ◽  
Streaming Data ◽  
Data Prediction

CPI Big Data Prediction Based on Wavelet Twin Support Vector Machine

2020 ◽  
pp. 2159013
Author(s):  
Yiqing Fan ◽  
Zhihui Sun
Keyword(s):  
Support Vector Machine ◽  
Big Data ◽  
Consumer Price Index ◽  
Low Frequency ◽  
Prediction Method ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Reconstruction Method ◽  
Data Prediction ◽  
Svm Model

In order to effectively improve the accuracy of Consumer Price Index (CPI) prediction so as to more truly reflect the overall level of the country’s macroeconomic situation, a CPI big data prediction method based on wavelet twin support vector machine (SVM) is proposed. First, the historical CPI data are decomposed into high-frequency part and low-frequency part by wavelet transform. Then a more advanced twin SVM is used to build a prediction model to obtain two kinds of prediction results. Finally, the wavelet reconstruction method is used to fuse the two kinds of prediction results to obtain the final CPI prediction results. The wavelet twin SVM model is used to fit and predict CPI index. Experimental results show that compared with the similar prediction methods, the proposed prediction method has higher fitting accuracy and smaller root mean square error.

Prediction of Uncertain Spatiotemporal Data Based on XML Integrated With Markov Chain

2019 ◽  
pp. 154-183
Author(s):  
Luyi Bai ◽  
Nan Li ◽  
Chengjia Sun ◽  
Yuan Zhao
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Data Management ◽  
Prediction Method ◽  
Research Field ◽  
Experimental Results ◽  
Spatiotemporal Data ◽  
Meteorological Field ◽  
Data Prediction ◽  
Spatiotemporal Data Model

Since XML could benefit data management greatly and Markov chains have an advantage in data prediction, the authors study the methodology of predicting uncertain spatiotemporal data based on XML integrated with Markov chain. To accomplish this, first, the researchers devise an uncertain spatiotemporal data model based on XML. Then, the researchers put forward the method based on Markov chains to predict spatiotemporal data, which has taken the uncertainty into consideration. Next, the researchers apply the prediction method to meteorological field. Finally, the experimental results demonstrate the advantages the authors approach. Such a method of prediction could broaden the research field of spatiotemporal data, and provide a significant reference in the study of forecasting uncertain spatiotemporal data.

scholarly journals Implementation of Resilient Methods to Predict Open Unemployment in Indonesia According to Higher Education Completed

2019 ◽  
Vol 3 (1) ◽  
pp. 163
Author(s):  
Widodo Saputra ◽  
Jaya Tata Hardinata ◽  
Anjar Wanto
Keyword(s):  
Network Architecture ◽  
Prediction Method ◽  
Architecture Model ◽  
Data Prediction ◽  
Architectural Model ◽  
Input Layer ◽  
Unemployment Data ◽  
The Government ◽  
The Right ◽  
Hidden Neurons

Unemployment is a big problem faced by the Indonesian people from year to year besides poverty. Therefore it is necessary to predict the level of open unemployment in Indonesia so that later the government and private parties have the right references and references to work together to overcome this problem. The prediction method used is Resilient Backpropagation which is one method of Artificial Neural Networks which is often used for data prediction. The research data used is open unemployment data according to the highest education completed in 2005-2018 based on the semester obtained from the website of the Indonesian Central Bureau of Statistics. Based on this data a network architecture model will be formed and determined, including 12-6-2, 12-12-2, 12-18-2, 12-24-2, 12-12-12-2, 12-12-18 -2, 12-18-18-2 and 12-18-24-2. From these 8 models after training and testing, the results show that the best architectural model is 12-18-2 (12 is the input layer, 18 is the number of hidden neurons and 2 is the output layer). The accuracy of the architectural model for semester 1 and semester 2 is 75% with an MSE value of 0.0022135087 and 0.0044974696

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