scholarly journals Assessment of visibility graph similarity as a synchronization measure for chaotic, noisy and stochastic time series

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Negar Ahmadi ◽  
Rene M. H. Besseling ◽  
Mykola Pechenizkiy
Keyword(s):  
Time Series ◽  
Visibility Graph ◽  
Graph Similarity ◽  
Stochastic Time Series ◽  
Stochastic Time

scholarly journals Identification of an unstable ARMA equation

2001 ◽  
Vol 7 (1) ◽  
pp. 97-112 ◽  
Author(s):  
Yulia R. Gel ◽  
Vladimir N. Fomin
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Infinite Order ◽  
Time Series Model ◽  
Identification Algorithm ◽  
Data Handling ◽  
Stochastic Time Series ◽  
Stochastic Time ◽  
Strongly Consistent ◽  
Minimal Phase

Usually the coefficients in a stochastic time series model are partially or entirely unknown when the realization of the time series is observed. Sometimes the unknown coefficients can be estimated from the realization with the required accuracy. That will eventually allow optimizing the data handling of the stochastic time series.Here it is shown that the recurrent least-squares (LS) procedure provides strongly consistent estimates for a linear autoregressive (AR) equation of infinite order obtained from a minimal phase regressive (ARMA) equation. The LS identification algorithm is accomplished by the Padé approximation used for the estimation of the unknown ARMA parameters.

Fractal and Multifractal in Stochastic Time Series

2020 ◽  
pp. 129-151
Author(s):  
Santo Banerjee ◽  
M K Hassan ◽  
Sayan Mukherjee ◽  
A Gowrisankar
Keyword(s):  
Time Series ◽  
Stochastic Time Series ◽  
Stochastic Time

A Recurrent Probabilistic Neural Network for EMG Pattern Recognition

2011 ◽  
pp. 130-153 ◽  
Author(s):  
Toshio Tsuji ◽  
Nan Bu ◽  
Osamu Fukuda
Keyword(s):  
Time Series ◽  
Pattern Recognition ◽  
Hidden Markov ◽  
Probabilistic Neural Network ◽  
Temporal Characteristic ◽  
Gaussian Mixture ◽  
Neural Computation ◽  
Emg Pattern Recognition ◽  
Stochastic Time Series ◽  
Stochastic Time

In the field of pattern recognition, probabilistic neural networks (PNNs) have been proven as an important classifier. For pattern recognition of EMG signals, the characteristics usually used are: (1) amplitude, (2) frequency, and (3) space. However, significant temporal characteristic exists in the transient and non-stationary EMG signals, which cannot be considered by traditional PNNs. In this article, a recurrent PNN, called recurrent log-linearized Gaussian mixture network (R-LLGMN), is introduced for EMG pattern recognition, with the emphasis on utilizing temporal characteristics. The structure of R-LLGMN is based on the algorithm of a hidden Markov model (HMM), which is a routinely used technique for modeling stochastic time series. Since R-LLGMN inherits advantages from both HMM and neural computation, it is expected to have higher representation ability and show better performance when dealing with time series like EMG signals. Experimental results show that R-LLGMN can achieve high discriminant accuracy in EMG pattern recognition.

Optimal Portfolio Strategy for Risk Management in Toll Road Forecasts and Investments

2017 ◽  
Vol 2670 (1) ◽  
pp. 83-94 ◽  
Author(s):  
Rohan Shah ◽  
Phani R. Jammalamadaka
Keyword(s):  
Time Series ◽  
Risk Management ◽  
A Priori ◽  
Risk Propensity ◽  
Performance Expectations ◽  
Forecast Performance ◽  
Toll Road ◽  
Trade Offs ◽  
Stochastic Time Series ◽  
Stochastic Time

The study leveraged modern portfolio theory and stochastic time series models to develop a risk management strategy for future traffic projections along brownfield toll facilities. Uncertainty in future traffic forecasts may raise concerns about performance reliability and revenue potential. Historical time series traffic data from brownfield corridors were used for developing econometric forecast estimates, and Monte Carlo simulation was used to quantify a priori risks or variance to develop optimal forecasts by using mean-variance optimization strategies. Numerical analysis is presented with historical toll transactions along the Massachusetts Turnpike system. Suggested diversification strategies were found to achieve better long-term forecast efficiencies with improved trade-offs between anticipated risks and returns. Planner and agency forecast performance expectations and risk propensity are thus jointly captured.

Comments on power spectra of discrete stochastic time series

1987 ◽  
Vol 119 (8) ◽  
pp. 388-390 ◽  
Author(s):  
M. Duong-Van ◽  
M.D. Feit
Keyword(s):  
Time Series ◽  
Power Spectra ◽  
Stochastic Time Series ◽  
Stochastic Time
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