scholarly journals Numerical Approximation of Some Infinite Gaussian Series and Integrals

2008 ◽  
Vol 13 (3) ◽  
pp. 397-415 ◽  
Author(s):  
M. Stoncelis ◽  
M. Vaičiulis
Keyword(s):  
Time Series ◽  
Numerical Computation ◽  
Infinite Series ◽  
Numerical Approximation ◽  
Asymptotic Variance ◽  
Process Time ◽  
Infinite Integral ◽  
Gaussian Series ◽  
Increment Ratio ◽  
Fractional Parameter

The paper deals with numerical computation of the asymptotic variance of the so-called increment ratio (IR) statistic and its modifications. The IR statistic is useful for estimation and hypothesis testing on fractional parameter H ∈ (0, 1) of random process (time series), see Surgailis et al. [1], Bardet and Surgailis [2]. The asymptotic variance of the IR statistic is given by an infinite integral (or infinite series) of 4-dimensional Gaussian integrals which depend on parameter H. Our method can be useful for numerical computation of other similar slowly convergent Gaussian integrals/series. Graphs and tables of approximate values of the variances σp2(H) and σˆp2(H), p = 1, 2 are included.

scholarly journals WDSchain: A Toolbox for Enhancing the Security Using Blockchain Technology in Water Distribution System

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1944
Author(s):  
Haitham H. Mahmoud ◽  
Wenyan Wu ◽  
Yonghao Wang
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Series Data ◽  
Process Time ◽  
Time Interval ◽  
Security Level ◽  
Data Validation ◽  
Simulated Time

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.

scholarly journals STATE ANALYSIS INTERNET OF THINGS DEVICES BASED ON BAGGING

T-Comm ◽  
2020 ◽  
Vol 14 (12) ◽  
pp. 45-50
Author(s):  
Mikhail E. Sukhoparov ◽  
◽  
Ilya S. Lebedev ◽  
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
The State ◽  
Point Of View ◽  
Operating Conditions ◽  
Process Time ◽  
K Nearest Neighbors ◽  
Computational Point ◽  
Iot Devices ◽  
The Internet Of Things

The development of IoT concept makes it necessary to search and improve models and methods for analyzing the state of remote autonomous devices. Due to the fact that some devices are located outside the controlled area, it becomes necessary to develop universal models and methods for identifying the state of low-power devices from a computational point of view, using complex approaches to analyzing data coming from various information channels. The article discusses an approach to identifying IoT devices state, based on parallel functioning classifiers that process time series received from elements in various states and modes of operation. The aim of the work is to develop an approach for identifying the state of IoT devices based on time series recorded during the execution of various processes. The proposed solution is based on methods of parallel classification and statistical analysis, requires an initial labeled sample. The use of several classifiers that give an answer "independently" from each other makes it possible to average the error by "collective" voting. The developed approach is tested on a sequence of classifying algorithms, to the input of which the time series obtained experimentally under various operating conditions were fed. Results are presented for a naive Bayesian classifier, decision trees, discriminant analysis, and the k nearest neighbors method. The use of a sequence of classification algorithms operating in parallel allows scaling by adding new classifiers without losing processing speed. The method makes it possible to identify the state of the Internet of Things device with relatively small requirements for computing resources, ease of implementation, and scalability by adding new classifying algorithms.

scholarly journals From the Streets to the Mountains: The Dynamics of Transition from a Protest Wave to an Insurgency in Kashmir

2014 ◽  
Vol 19 (3) ◽  
pp. 309-327 ◽  
Author(s):  
Tijen Demirel-Pegg
Keyword(s):  
Time Series ◽  
Civil Wars ◽  
Transition Process ◽  
External Support ◽  
Process Time ◽  
Key Factors ◽  
Comprehensive Understanding ◽  
State Repression ◽  
Major Shift ◽  
External Actors

This study investigates the dynamics of transition from a peaceful protest wave to a violent insurgency. It examines the causal path leading to a major shift in the intensity of a protest wave and argues that the transition is the product of the interactions between the dissidents, the state, and external actors. By studying the protest wave in Kashmir (1979-88), it identifies state repression and external support as the key factors driving the transition process. Time series analysis is used to analyze the original empirical evidence collected through content analysis. By providing a comprehensive understanding of the origins of the insurgency in Kashmir, this study shows that protest waves and civil wars are intimately linked.

Basic Fourier series

1979 ◽  
Vol 369 (1736) ◽  
pp. 115-136 ◽  
Keyword(s):  
Time Series ◽  
Fourier Series ◽  
Stochastic Processes ◽  
Numerical Approximation ◽  
Basic Number ◽  
Second Order ◽  
Orthogonal Functions ◽  
Sturm Liouville

The concept of basic number is applied to the development of a simple analogue of the Sturm–Liouville system of the second order. This is then employed to deduce a family of q -orthogonal functions, which leads to a generalization of the Fourier and Fourier–Bessel expansions. The numerical approximation of basic integrals is discussed and some aspects of the evaluation of C a (q; x) are mentioned. A few of the zeros of this function are listed, and, in conclusion, an indication is given of the possibility of applying the analysis presented in this paper to thé study of stochastic processes and time-series.

scholarly journals Using RGISTools to Estimate Water Levels in Reservoirs and Lakes

2020 ◽  
Vol 12 (12) ◽  
pp. 1934 ◽  
Author(s):  
Ana F. Militino ◽  
Manuel Montesino-SanMartin ◽  
Unai Pérez-Goya ◽  
M. Dolores Ugarte
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Water Levels ◽  
Process Time ◽  
Landsat 8 ◽  
Northern Spain ◽  
Software Environment ◽  
Efficient Manner ◽  
Radar Images

The combination of freely accessible satellite imagery from multiple programs improves the spatio-temporal coverage of remote sensing data, but it exhibits barriers regarding the variety of web services, file formats, and data standards. Ris an open-source software environment with state-of-the-art statistical packages for the analysis of optical imagery. However, it lacks the tools for providing unified access to multi-program archives to customize and process the time series of images. This manuscript introduces RGISTools, a new software that solves these issues, and provides a working example on water mapping, which is a socially and environmentally relevant research field. The case study uses a digital elevation model and a rarely assessed combination of Landsat-8 and Sentinel-2 imagery to determine the water level of a reservoir in Northern Spain. The case study demonstrates how to acquire and process time series of surface reflectance data in an efficient manner. Our method achieves reasonably accurate results, with a root mean squared error of 0.90 m. Future improvements of the package involve the expansion of the workflow to cover the processing of radar images. This should counteract the limitation of the cloud coverage with multi-spectral images.

Gaussian Process Time Series Model for Life Prognosis of Metallic Structures

2009 ◽  
Vol 20 (8) ◽  
pp. 887-896 ◽  
Author(s):  
Subhasish Mohanty ◽  
Santanu Das ◽  
Aditi Chattopadhyay ◽  
Pedro Peralta
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Time Series Model ◽  
Process Time ◽  
Metallic Structures ◽  
Life Prognosis

scholarly journals A SIMPLE EFFICIENT INSTRUMENTAL VARIABLE ESTIMATOR FOR PANEL AR(p) MODELS WHEN BOTHNANDTARE LARGE

2009 ◽  
Vol 25 (3) ◽  
pp. 873-890 ◽  
Author(s):  
Kazuhiko Hayakawa
Keyword(s):  
Time Series ◽  
Lower Bound ◽  
Asymptotic Distribution ◽  
Cross Section ◽  
Simulation Study ◽  
Instrumental Variable ◽  
Asymptotic Variance ◽  
The Cross ◽  
Instrumental Variable Estimator ◽  
Normally Distributed

In this paper, we show that for panel AR(p) models, an instrumental variable (IV) estimator with instruments deviated from past means has the same asymptotic distribution as the infeasible optimal IV estimator when bothNandT, the dimensions of the cross section and time series, are large. If we assume that the errors are normally distributed, the asymptotic variance of the proposed IV estimator is shown to attain the lower bound when bothNandTare large. A simulation study is conducted to assess the estimator.

TESTING CHAOS OF TIME SERIES: PORTMANTEAU STATISTICS UNDER AN ORDER TRANSFORMATION

2001 ◽  
Vol 11 (06) ◽  
pp. 1761-1769 ◽  
Author(s):  
DEJIAN LAI
Keyword(s):  
Time Series ◽  
Null Hypothesis ◽  
Random Variable ◽  
Asymptotic Distributions ◽  
Test Statistics ◽  
Chi Square ◽  
Gaussian Series ◽  
Low Dimensional ◽  
Portmanteau Statistics ◽  
Direct Use

This paper studies several portmanteau test statistics with a nonparametric order transformation for distinguishing independent and identically distributed (i.i.d.) random processes from noisy chaotic time series. These portmanteau test statistics are asymptotically distributed as a chi-square random variable under the null hypothesis of i.i.d. Gaussian series. In this Letter, we show that the asymptotic distributions of these portmanteau test statistics on the transformed series are still chi-square under the null hypothesis. The simulations indicate that direct use of these portmanteau test statistics yields low power in identifying chaos. However, with the proposed order transformation, the simulations show that these test statistics are still effective for identifying noisy low dimensional chaos in some cases.

scholarly journals Gaussian Process Time-Series Models for Structures under Operational Variability

2017 ◽  
Vol 3 ◽  
Author(s):  
Luis David Avendaño-Valencia ◽  
Eleni N. Chatzi ◽  
Ki Young Koo ◽  
James M. W. Brownjohn
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Process Time ◽  
Time Series Models
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