scholarly journals Time series analysis for water resources management - application to observed and simulated time-series of the groundwater flow numerical model of the coastal plain of Cecina

2021 ◽  
Vol 10 (3) ◽  
pp. 31-38
Author(s):  
Stefano Menichetti ◽  
Stefano Tessitore
Keyword(s):  
Time Series ◽  
Groundwater Flow ◽  
Coastal Plain ◽  
Transient Regime ◽  
Management Tool ◽  
Flow Models ◽  
Time Series Decomposition ◽  
Management Application ◽  
Simulated Time ◽  
Simulated Time Series

This paper highlights the potentiality of the time series decomposition applied to transient regime groundwater flow models, as water balance management tool. In particular, this work presents results obtained by applying statistical analysis to some observed time series and to time series derived from the groundwater flow model of the coastal plain of Cecina (Tuscany region, Italy), developed in transient regime within the period 2005-2017. The time series of rainfall, river stage and hydraulic heads were firstly analysed, and then time series decomposition was applied to the “accumulated net storage”, to finally discern and quantify two meaningful components of the groundwater budget, the regulatory reserve (Wr = 22 Mm3) and the seasonal resource (Wd = 2.5 Mm3). These values compared with withdrawal volumes (average of 6.4 Mm3/y within the period 2005-2017) allowed to highlight potentially critical balance conditions, especially in periods with repeated negative climatic trends. Operational monitoring and modeling as following corrective and planning actions for the groundwater resource are suggested.

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

Singular Spectrum Analysis Based on L1-Norm

2016 ◽  
Vol 15 (01) ◽  
pp. 1650009 ◽  
Author(s):  
Mahdi Kalantari ◽  
Masoud Yarmohammadi ◽  
Hossein Hassani
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Frobenius Norm ◽  
L1 Norm ◽  
Empirical Results ◽  
Singular Spectrum ◽  
Simulated Time ◽  
Simulated Time Series ◽  
Basic Version

In recent years, the singular spectrum analysis (SSA) technique has been further developed and increasingly applied to solve many practical problems. The aim of this research is to introduce a new version of SSA based on [Formula: see text]-norm. The performance of the proposed approach is assessed by applying it to various real and simulated time series, especially with outliers. The results are compared with those obtained using the basic version of SSA which is based on the Frobenius norm or [Formula: see text]-norm. Different criteria are also examined including reconstruction errors and forecasting performances. The theoretical and empirical results confirm that SSA based on [Formula: see text]-norm can provide better reconstruction and forecasts in comparison to basic SSA when faced with time series which are polluted by outliers.

Tests for transient means in simulated time series

1994 ◽  
Vol 41 (2) ◽  
pp. 171-187 ◽  
Author(s):  
David Goldsman ◽  
Lee W. Schruben ◽  
James J. Swain
Keyword(s):  
Time Series ◽  
Simulated Time ◽  
Simulated Time Series

A SIMPLE MODEL FOR STOCKS MARKETS

2002 ◽  
Vol 13 (05) ◽  
pp. 639-644 ◽  
Author(s):  
JUAN R. SANCHEZ
Keyword(s):  
Time Series ◽  
Simple Model ◽  
Time Evolution ◽  
Stock Markets ◽  
Stock Price ◽  
New Model ◽  
Simulated Time ◽  
Simulated Time Series

A new model for stock markets using integer values for each stock price is presented. In contrast with previously reported models, the variables used in the model are not of binary type, but of more general integer type. It is shown how the behavior of the noise and fundamentalists traders can be taken into account simultaneously in the time evolution of each stock price. The simulated time series generated by the model is analyzed in different ways order to compare parameters with those of real markets.

scholarly journals Estimating the power to detect a change caused by a vaccine from time series data

2020 ◽  
Vol 4 ◽  
pp. 27
Author(s):  
Daniel M. Weinberger ◽  
Joshua L. Warren
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
A Priori ◽  
Model Misspecification ◽  
Series Data ◽  
Real World Data ◽  
Vaccination Programs ◽  
Vaccine Introduction ◽  
Simulated Time ◽  
Simulated Time Series

When evaluating the effects of vaccination programs, it is common to estimate changes in rates of disease before and after vaccine introduction. There are a number of related approaches that attempt to adjust for trends unrelated to the vaccine and to detect changes that coincide with introduction. However, characteristics of the data can influence the ability to estimate such a change. These include, but are not limited to, the number of years of available data prior to vaccine introduction, the expected strength of the effect of the intervention, the strength of underlying secular trends, and the amount of unexplained variability in the data. Sources of unexplained variability include model misspecification, epidemics due to unidentified pathogens, and changes in ascertainment or coding practice among others. In this study, we present a simple simulation framework for estimating the power to detect a decline and the precision of these estimates. We use real-world data from a pre-vaccine period to generate simulated time series where the vaccine effect is specified a priori. We present an interactive web-based tool to implement this approach. We also demonstrate the use of this approach using observed data on pneumonia hospitalization from the states in Brazil from a period prior to introduction of pneumococcal vaccines to generate the simulated time series. We relate the power of the hypothesis tests to the number of cases per year and the amount of unexplained variability in the data and demonstrate how fewer years of data influence the results.

A state–space mixture approach for estimating catastrophic events in time series data

2007 ◽  
Vol 64 (6) ◽  
pp. 899-910 ◽  
Author(s):  
Eric J Ward ◽  
Ray Hilborn ◽  
Rod G Towell ◽  
Leah Gerber
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
State Space ◽  
Normal Component ◽  
Mixture Distribution ◽  
Series Data ◽  
Catastrophic Events ◽  
Process Error ◽  
Simulated Time ◽  
Simulated Time Series

Catastrophic events are considered a major contributor to extinction threats, yet are rarely explicitly estimated in population models. We extend the basic state–space population dynamics model to include a mixture distribution for the process error. The mixture distribution consists of a "normal" component, representing regular process error variability, and a "catastrophic" component, representing rare events that negatively affect the population. Direct estimation of parameters is rarely possible using a single time series; however, estimation is possible when time series are combined in hierarchical models. We apply the catastrophic state–space model to simulated time series of abundance from simple, nonlinear population dynamics models. Applications of the model to these simulated time series indicate that population parameters (such as the carrying capacity or growth rate) and observation and process errors are estimated robustly when appropriate time series are available. Our simulations indicate that the power to detect a catastrophe is also a function of the strength of catastrophes and the magnitude of observation and process errors. To illustrate one potential application of this model, we apply the state–space catastrophic model to four west coast populations of northern fur seals (Callorhinus ursinus).

Measurement of Chaotic Behavior of Operator Stabilizing an Inverted Pendulum and Its Fuzzy Identification from Time Series Data

2001 ◽  
Vol 13 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Yoshihiko Kawazoe ◽  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Chaotic Behavior ◽  
Series Data ◽  
Experimental Time ◽  
Experimental Time Series ◽  
Simulated Time ◽  
Simulated Time Series

This paper investigates the identification of the chaotic characteristics of human operation with individual difference and the skill difference from the experimental time series data by utilizing fuzzy inference. It shows how to construct rules automatically for a fuzzy controller from experimental time series data of each trial of each operator to identify a controller from human-generated decision-making data. The characteristics of each operator trial were identified fairly well from experimental time series data by utilizing fuzzy reasoning. It was shown that the estimated maximum Lyapunov exponents of simulated time series data using an identified fuzzy controller were positive against embedding dimensions, which means a chaotic phenomenon. It was also recognized that the simulated human behavior have a large amount of disorder according to the result of estimated entropy from the simulated time, series data.

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