Reconstructing System Dynamics and Causal Interactions from Complex Time Series Data

2017 ◽  
Author(s):  
Ray Huffaker
Keyword(s):  
Time Series ◽  
System Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Complex Time

Statistical Inference for Complex Time Series Data

2013 ◽  
Vol 10 (3) ◽  
pp. 2749-2823
Author(s):  
Rainer Dahlhaus ◽  
Oliver Linton ◽  
Wei-Biao Wu ◽  
Qiwei Yao
Keyword(s):  
Time Series ◽  
Statistical Inference ◽  
Time Series Data ◽  
Series Data ◽  
Complex Time

AN INTEGRATED DATA CHARACTERISTIC TESTING SCHEME FOR COMPLEX TIME SERIES DATA EXPLORATION

2013 ◽  
Vol 12 (03) ◽  
pp. 491-521 ◽  
Author(s):  
LING TANG ◽  
LEAN YU ◽  
FANGTAO LIU ◽  
WEIXUAN XU
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Data Exploration ◽  
Series Data ◽  
Economic Time Series ◽  
Noise Pattern ◽  
Complex Time ◽  
Testing Scheme ◽  
Sample Data ◽  
Economic Time

In this paper, an integrated data characteristic testing scheme is proposed for complex time series data exploration so as to select the most appropriate research methodology for complex time series modeling. Based on relationships across different data characteristics, data characteristics of time series data are divided into two main categories: nature characteristics and pattern characteristics in this paper. Accordingly, two relevant tasks, nature determination and pattern measurement, are involved in the proposed testing scheme. In nature determination, dynamics system generating the time series data is analyzed via nonstationarity, nonlinearity and complexity tests. In pattern measurement, the characteristics of cyclicity (and seasonality), mutability (or saltation) and randomicity (or noise pattern) are measured in terms of pattern importance. For illustration purpose, four main Chinese economic time series data are used as testing targets, and the data characteristics hidden in these time series data are thoroughly explored by using the proposed integrated testing scheme. Empirical results reveal that the natures of all sample data demonstrate complexity in the phase of nature determination, and in the meantime the main pattern of each time series is captured based on the pattern importance, indicating that the proposed scheme can be used as an effective data characteristic testing tool for complex time series data exploration from a comprehensive perspective.

Inferring groundwater system dynamics from hydrological time-series data

2010 ◽  
Vol 55 (4) ◽  
pp. 593-608 ◽  
Author(s):  
Angelo Doglioni ◽  
Davide Mancarella ◽  
Vincenzo Simeone ◽  
Orazio Giustolisi
Keyword(s):  
Time Series ◽  
System Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Groundwater System

scholarly journals Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xue-Bo Jin ◽  
Jia-Hui Zhang ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Decomposition ◽  
Time Series Data ◽  
Bayesian Optimization ◽  
Data Driven ◽  
Practical Significance ◽  
Series Data ◽  
Complex Time ◽  
Modeling And Analysis ◽  
Multicomponent Data

Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend. Firstly, the data is decomposed by wavelet transform to reduce the complexity of the time series data. The Gated Recurrent Unit (GRU) network is trained as a submodel for each decomposition component. The hyperparameters of wavelet decomposition and each submodel are optimized with Bayesian sequence model-based optimization (SMBO) to develop the modeling accuracy. Finally, the results of all submodels are added to obtain forecasting results. The PM2.5 data collected by the US Air Quality Monitoring Station is used for experiments. By comparing with other networks, it can be found that the proposed method outperforms well in the multisteps forecasting task for the complex time series.

Why Study Nonlinear Time Series Analysis?

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
System Dynamics ◽  
Real World ◽  
Time Series Data ◽  
Nonlinear Time Series ◽  
Series Data ◽  
Nonlinear Time Series Analysis ◽  
Deterministic System ◽  
Series Analysis

Nonlinear Time Series Analysis (NLTS) provides a mathematically rigorous collection of techniques designed to reconstruct real-world system dynamics from time series data on a single variable or multiple causally-related variables. NLTS facilitates scientific inquiry that emphasizes strong supportive evidence, well-conducted and thorough inquiry, and realism. Data provide an essential evidentiary portal to a reality to which we have only limited access. Random-appearing data do not prove that underlying dynamic process are subject to exogenous inherently-random forces. The possibility exists that observed volatility is generated by inherently-unstable, deterministic, and nonlinear real-world dynamic systems. NLTS allows the data to speak regarding which type of system dynamics generated them. It is capable of detecting linear as well as nonlinear deterministic system dynamics, and diagnosing the presence of linear stochastic dynamics. Our objective is to use NLTS to uncover the structure best corresponding to reality whether it be linear, nonlinear, deterministic, or stochastic. Accurate diagnosis of real-world dynamics from observed data is crucial to develop valid theory, and to formulate effective public policy based on theory.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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