Model-based clustering of regression time series data via APECM-an AECM algorithm sung to an even faster beat

2011 ◽  
Vol 4 (6) ◽  
pp. 567-578 ◽  
Author(s):  
Wei-Chen Chen ◽  
Ranjan Maitra
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Model Based Clustering ◽  
Model Based

scholarly journals Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data

2017 ◽  
Vol 29 (4) ◽  
pp. 990-1020 ◽  
Author(s):  
Hien D. Nguyen ◽  
Geoffrey J. McLachlan ◽  
Pierre Orban ◽  
Pierre Bellec ◽  
Andrew L. Janke
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Expectation Maximization Algorithm ◽  
Resting State Fmri ◽  
Series Data ◽  
Ml Estimation ◽  
Model Based Clustering ◽  
Model Based ◽  
Ml Estimator ◽  
Maximum Pseudolikelihood

Mixture of autoregressions (MoAR) models provide a model-based approach to the clustering of time series data. The maximum likelihood (ML) estimation of MoAR models requires evaluating products of large numbers of densities of normal random variables. In practical scenarios, these products converge to zero as the length of the time series increases, and thus the ML estimation of MoAR models becomes infeasible without the use of numerical tricks. We propose a maximum pseudolikelihood (MPL) estimation approach as an alternative to the use of numerical tricks. The MPL estimator is proved to be consistent and can be computed with an EM (expectation-maximization) algorithm. Simulations are used to assess the performance of the MPL estimator against that of the ML estimator in cases where the latter was able to be calculated. An application to the clustering of time series data arising from a resting state fMRI experiment is presented as a demonstration of the methodology.

scholarly journals Pengujian Efek Fisher:Pengaruh Ekspektasi Inflasi Dan Kegiatan Ekonomi Terhadap Tingkat Bunga Nominal Di Indonesia

2019 ◽  
Author(s):  
Dian Pratiwi
Keyword(s):  
Time Series ◽  
Economic Activity ◽  
Time Series Data ◽  
Series Data ◽  
Fisher Effect ◽  
Model Based ◽  
Expected Inflation ◽  
Effect Theory ◽  
Nominal Rate

The purpose of this paper is to investigate the effect of inflation and economic activity to nominal rate. A model based on Fisher Effect and used time series data for the period of 2010-2012. The finding suggests that expected inflation and economic activity have significant effect on the nominal rate as dependent variable. As the limitation, the data used in this paper are limited to three years time series data. A more detail analysis would use data more completely. The findings of the study clearly demonstrate the Fisher Effect theory. Keywords: Fisher Effect, Expected Inflation, Economic Activity, Nominal Rate

scholarly journals Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast

2019 ◽  
Vol 9 (3) ◽  
pp. 423 ◽  
Author(s):  
Shenghui Zhang ◽  
Yuewei Liu ◽  
Jianzhou Wang ◽  
Chen Wang
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Linear Models ◽  
Time Series Data ◽  
Mean Absolute Percentage Error ◽  
Series Data ◽  
Percentage Error ◽  
Combined Model ◽  
Absolute Percentage Error ◽  
Model Based

Wind power is an important part of a power system, and its use has been rapidly increasing as compared with fossil energy. However, due to the intermittence and randomness of wind speed, system operators and researchers urgently need to find more reliable wind-speed prediction methods. It was found that the time series of wind speed not only has linear characteristics, but also nonlinear. In addition, most methods only consider one criterion or rule (stability or accuracy), or one objective function, which can lead to poor forecasting results. So, wind-speed forecasting is still a difficult and challenging problem. The existing forecasting models based on combination-model theory can adapt to some time-series data and overcome the shortcomings of the single model, which achieves poor accuracy and instability. In this paper, a combined forecasting model based on data preprocessing, a nondominated sorting genetic algorithm (NSGA-III) with three objective functions and four models (two hybrid nonlinear models and two linear models) is proposed and was successfully applied to forecasting wind speed, which not only overcomes the issue of forecasting accuracy, but also solves the difficulties of forecasting stability. The experimental results show that the stability and accuracy of the proposed combined model are better than the single models, improving the mean absolute percentage error (MAPE) range from 0.007% to 2.31%, and the standard deviation mean absolute percentage error (STDMAPE) range from 0.0044 to 0.3497.

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