scholarly journals Central Prediction System for Time Series Comparison and Analysis of Water Usage Data

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 10342-10351
Author(s):  
Mingeun Ji ◽  
Gangman Yi ◽  
Jaehee Jung
Keyword(s):  
Time Series ◽  
Prediction System ◽  
Water Usage ◽  
Comparison And Analysis ◽  
Time Series Comparison ◽  
Usage Data

scholarly journals Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

2021 ◽  
Vol 7 ◽  
pp. e744
Author(s):  
Si Thu Aung ◽  
Yodchanan Wongsawat
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Performance Measures ◽  
Multiple Scales ◽  
Multivariate Time Series ◽  
World Population ◽  
Prediction System ◽  
Seizure Prediction ◽  
Feature Extraction Method ◽  
Wide Range

Epilepsy is a common neurological disease that affects a wide range of the world population and is not limited by age. Moreover, seizures can occur anytime and anywhere because of the sudden abnormal discharge of brain neurons, leading to malfunction. The seizures of approximately 30% of epilepsy patients cannot be treated with medicines or surgery; hence these patients would benefit from a seizure prediction system to live normal lives. Thus, a system that can predict a seizure before its onset could improve not only these patients’ social lives but also their safety. Numerous seizure prediction methods have already been proposed, but the performance measures of these methods are still inadequate for a complete prediction system. Here, a seizure prediction system is proposed by exploring the advantages of multivariate entropy, which can reflect the complexity of multivariate time series over multiple scales (frequencies), called multivariate multiscale modified-distribution entropy (MM-mDistEn), with an artificial neural network (ANN). The phase-space reconstruction and estimation of the probability density between vectors provide hidden complex information. The multivariate time series property of MM-mDistEn provides more understandable information within the multichannel data and makes it possible to predict of epilepsy. Moreover, the proposed method was tested with two different analyses: simulation data analysis proves that the proposed method has strong consistency over the different parameter selections, and the results from experimental data analysis showed that the proposed entropy combined with an ANN obtains performance measures of 98.66% accuracy, 91.82% sensitivity, 99.11% specificity, and 0.84 area under the curve (AUC) value. In addition, the seizure alarm system was applied as a postprocessing step for prediction purposes, and a false alarm rate of 0.014 per hour and an average prediction time of 26.73 min before seizure onset were achieved by the proposed method. Thus, the proposed entropy as a feature extraction method combined with an ANN can predict the ictal state of epilepsy, and the results show great potential for all epilepsy patients.

Volatility Modeling with Leverage Effect under Laplace Errors

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhengjun Jiang ◽  
Weixuan Xia
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Financial Time Series ◽  
Laplace Distribution ◽  
Leverage Effect ◽  
Financial Returns ◽  
Financial Time ◽  
Error Distributions ◽  
Time Series Comparison ◽  
Unconditional Variance

AbstractThis paper discusses four GARCH-type models (A-GARCH, NA-GARCH, GJR-GARCH, and E-GARCH) in representing volatility of financial returns with leverage effect. In these models, errors are assumed to follow a Laplace distribution in order to deal with the typical leptokurtic feature of financial returns. The properties of these models are analyzed theoretically in terms of unconditional variance, kurtosis, autocorrelation function and news impact, and are further examined in the applications to real financial time series. Comparison is made with other choices of error distributions such as normal, Student-5, and Student-7 distributions, respectively. We also conduct residual analyses to justify the choice of error distributions and find that Laplace-E-GARCH model still performs very well. Our main purpose is to study and compare the proposed models’ relative adequacies and underlying limitations.

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