Synchronization and control of spatiotemporal chaos using time-series data from local regions

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

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Detection of Thermoacoustic Instabilities Via Nonparametric Bayesian Markov Modeling of Time-Series Data

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4037288 ◽

2017 ◽

Vol 140 (2) ◽

Author(s):

Sihan Xiong ◽

Sudeepta Mondal ◽

Asok Ray

Keyword(s):

Time Series ◽

Real Time ◽

Time Series Data ◽

Data Driven ◽

Series Data ◽

Markov Modeling ◽

Bayesian Nonparametric ◽

Dynamic Data ◽

Thermoacoustic Instabilities ◽

And Control

Real-time detection and decision and control of thermoacoustic instabilities in confined combustors are challenging tasks due to the fast dynamics of the underlying physical process. The objective here is to develop a dynamic data-driven algorithm for detecting the onset of instabilities with short-length time-series data, acquired by available sensors (e.g., pressure and chemiluminescence), which will provide sufficient lead time for active decision and control. To this end, this paper proposes a Bayesian nonparametric method of Markov modeling for real-time detection of thermoacoustic instabilities in gas turbine engines; the underlying algorithms are formulated in the symbolic domain and the resulting patterns are constructed from symbolized pressure measurements as probabilistic finite state automata (PFSA). These PFSA models are built upon the framework of a (low-order) finite-memory Markov model, called the D-Markov machine, where a Bayesian nonparametric structure is adopted for: (i) automated selection of parameters in D-Markov machines and (ii) online sequential testing to provide dynamic data-driven and coherent statistical analyses of combustion instability phenomena without solely relying on computationally intensive (physics-based) models of combustion dynamics. The proposed method has been validated on an ensemble of pressure time series from a laboratory-scale combustion apparatus. The results of instability prediction have been compared with those of other existing techniques.

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Do banking sector reforms cause economic growth?: Empirical evidence from Africa’s largest economy

Corporate Ownership and Control ◽

10.22495/cocv13i1c5p3 ◽

2015 ◽

Vol 13 (1) ◽

pp. 553-564

Author(s):

Andy Titus Okwu ◽

Olusola Babatunde Falaiye ◽

Rowland Tochukwu Obiakor ◽

Ajibola Joseph Olusegun

Keyword(s):

Economic Growth ◽

Time Series ◽

Banking Sector ◽

Time Series Data ◽

Series Data ◽

Real Sector ◽

Reform Strategies ◽

Long Run ◽

Short Run ◽

And Control

This paper employed time series data on relevant empirical diagnostics to examine banking sector growth-led nexus within the context of Africa’s largest economy, Nigeria. Diagnostics established stationarity of banking sector indicators and control variables at first difference. Findings showed no causal relationships between banking sector reforms and economic growth in the short-run and that, though liberalisation in particular did not Granger-cause growth of the economy during the study period, banking sector reforms caused growth of the real sector of the Nigerian economy. Hence, the caveat was that long-run growth effects of banking sector reforms on real sectors of economies are functions of policy targets of such banking or financial sectors reform strategies. Consequently, articulation of banking and financial sectors reforms within long-run rather than short-run perspectives and complementarity of liberalisation were recommended.

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Analyzing and Forecasting HIV Data Using Hybrid Time Series Models

Asian Journal of Probability and Statistics ◽

10.9734/ajpas/2018/v2i328793 ◽

2019 ◽

pp. 1-12

Author(s):

Liming Xie

Keyword(s):

Time Series ◽

Time Series Data ◽

Hybrid Methods ◽

Moving Average ◽

Series Data ◽

People Living With Hiv ◽

The World ◽

Linear And Nonlinear ◽

Original Time ◽

And Control

In real work, we often confront complete linear and nonlinear time series data. But some time series are not pure linear and nonlinear, or complicated one, we need apply two or more models to analyze and predict them. It is necessary to explore and find some novel time series hybrid methods to solve it. Human Immunodeficiency and Virus (HIV) is one of intractable and trouble diseases in the world. Thus, the author of this article wants to analyze and probe into some novel time series methods to get breaking breach in the epidemiology that find some rules in the incidence, distribution, pathogen, and control of HIV in a population. In this article, to find the best model, auto.arima function is applied to the original time series data to determine autoregressive integrated moving average, ARIMA(0,0,0); ARIMA and generalized autoregressive conditional heteroskedasticity (GARCH), that is, ARIMA-GARCH (1,1) model is used to analyze numbers of people living with HIV for the data of HIV in the world such some important parameters as mu, ar1, ar2, omega, alpha 1, or beta 1 and some specific tests, for example, Jarque-Bera Test, Shapiro-Wilk Test, Ljung-Box Test, etc. Using ARIMA (0, 0, 0) and SARIMA (0,2), seasonal ARIMA, to predict the future values and trends after 2015. Both suggest identical results.

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Good governance and crime rates in Malaysia

International Journal of Social Economics ◽

10.1108/ijse-05-2014-0096 ◽

2016 ◽

Vol 43 (3) ◽

pp. 308-320 ◽

Cited By ~ 5

Author(s):

Muzafar Shah Habibullah ◽

Badariah H.Din ◽

Baharom Abdul Hamid

Keyword(s):

Time Series ◽

Time Series Data ◽

Good Governance ◽

Negative Relationship ◽

Political Stability ◽

Crime Rates ◽

Series Data ◽

Content Type ◽

Long Run ◽

And Control

Purpose – The purpose of this paper is to relate the quality of governance with crime in Malaysia. The study also identifies the best good governance tool to fight against crime in Malaysia. Design/methodology/approach – The study uses time-series data on crime rates and six measures of governance: voice and accountability, political stability, government effectiveness, regulatory quality, rule of law and control of corruption. In this study the authors employed the popular autoregressive distributed lagged modeling approach to estimate the long-run model of crime and governance. Findings – The authors test the hypothesis that good governance lowers crime rates (total crime, violent and property crimes). The results suggest a negative relationship between crime rates and good governance in Malaysia. This suggests that good governance reduces crime rates in Malaysia. Research limitations/implications – The limitations of this study is the short time-series used in the analysis which is from 1996 to 2009. Practical implications – This study provides evidence that the practice of good governance, for example, lower corruption, good policing and judicial system can mitigate crime in Malaysia. Social implications – The implementation of good governance will protect property right of individuals, business sector and the society as a whole, and this will enhance prosperity of a nation. Originality/value – This study provide the first empirical evidence that linking between crime and good governance in Malaysia.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

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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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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