scholarly journals Mamdani-Larsen fuzzy system based on expectation maximization algorithm and its applications to time series prediction

2009 ◽  
Vol 58 (1) ◽  
pp. 107
Author(s):  
Zhang Qin-Li ◽  
Wang Shi-Tong
Keyword(s):  
Time Series ◽  
Expectation Maximization ◽  
Fuzzy System ◽  
Time Series Prediction ◽  
Expectation Maximization Algorithm

ANNEALED CHAOTIC LEARNING FOR TIME SERIES PREDICTION IN IMPROVED NEURO-FUZZY NETWORK WITH FEEDBACKS

2009 ◽  
Vol 08 (04) ◽  
pp. 429-444 ◽  
Author(s):  
CATHERINE VAIRAPPAN ◽  
SHANGCE GAO ◽  
ZHENG TANG ◽  
HIROKI TAMURA
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Chaotic Dynamics ◽  
Time Series Prediction ◽  
Data Series ◽  
Temporal Data ◽  
Optimal Solutions ◽  
Benchmark Data ◽  
Neuro Fuzzy ◽  
Fuzzy Network

A new version of neuro-fuzzy system of feedbacks with chaotic dynamics is proposed in this work. Unlike the conventional neuro-fuzzy, improved neuro-fuzzy system with feedbacks is better able to handle temporal data series. By introducing chaotic dynamics into the feedback neuro-fuzzy system, the system has richer and more flexible dynamics to search for near-optimal solutions. In the experimental results, performance and effectiveness of the presented approach are evaluated by using benchmark data series. Comparison with other existing methods shows the proposed method for the neuro-fuzzy feedback is able to predict the time series accurately.

Blind source separation with time series variational Bayes expectation maximization algorithm

2012 ◽  
Vol 22 (1) ◽  
pp. 17-33 ◽  
Author(s):  
Shijun Sun ◽  
Chenglin Peng ◽  
Wensheng Hou ◽  
Jun Zheng ◽  
Yingtao Jiang ◽  
...  
Keyword(s):  
Time Series ◽  
Blind Source Separation ◽  
Expectation Maximization ◽  
Source Separation ◽  
Expectation Maximization Algorithm ◽  
Variational Bayes

scholarly journals Diagnostic Analytics for an Autoregressive Model under the Skew-Normal Distribution

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 693 ◽  
Author(s):  
Yonghui Liu ◽  
Guohua Mao ◽  
Víctor Leiva ◽  
Shuangzhe Liu ◽  
Alejandra Tapia
Keyword(s):  
Time Series ◽  
Normal Distribution ◽  
Expectation Maximization ◽  
Autoregressive Model ◽  
Expectation Maximization Algorithm ◽  
Model Fit ◽  
Simulation Studies ◽  
Skew Normal Distribution ◽  
Financial Return ◽  
Skew Normal

Autoregressive models have played an important role in time series. In this paper, an autoregressive model based on the skew-normal distribution is considered. The estimation of its parameters is carried out by using the expectation–maximization algorithm, whereas the diagnostic analytics are conducted by means of the local influence method. Normal curvatures for the model under four perturbation schemes are established. Simulation studies are conducted to evaluate the performance of the proposed procedure. In addition, an empirical example involving weekly financial return data are analyzed using the procedure with the proposed diagnostic analytics, which has improved the model fit.

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