Stochastic Modeling of Experimental Chaotic Time Series

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

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Particle Swarm optimization based Neural Network Model for Chaotic Time Series Forecasting

2020 12th International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci49185.2020.9177737 ◽

2020 ◽

Author(s):

Xin Li ◽

Weijie Ren ◽

Jingying Zhao ◽

Min Han

Keyword(s):

Neural Network ◽

Time Series ◽

Particle Swarm Optimization ◽

Network Model ◽

Neural Network Model ◽

Particle Swarm ◽

Time Series Forecasting ◽

Chaotic Time Series ◽

Swarm Optimization

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Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas

Water ◽

10.3390/w13162156 ◽

2021 ◽

Vol 13 (16) ◽

pp. 2156

Author(s):

George Pouliasis ◽

Gina Alexandra Torres-Alves ◽

Oswaldo Morales-Napoles

Keyword(s):

Time Series ◽

Stochastic Modeling ◽

Copula Models ◽

Multiple Processes ◽

Spatial Dependencies ◽

Probabilistic Dependence ◽

Temporal And Spatial ◽

Vine Copula ◽

Synthetic Time Series ◽

Hydroclimatic Variables

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated.

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