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2018
Analysis of epidemic time series is a large endeavor because of the richness of dynamical patterns and plentitude of historical data (Rohani and King 2010). A wide range of tools are used, some of which are borrowed from mainstream statistics other of which are “custom made.” The classic “mainstream” methods belong to two categories: the so-called time-
Abdulkader Aljandali, Motasam Tatahi
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Analysis of epidemic time series is a large endeavor because of the richness of dynamical patterns and plentitude of historical data (Rohani and King 2010). A wide range of tools are used, some of which are borrowed from mainstream statistics other of which are “custom made.” The classic “mainstream” methods belong to two categories: the so-called time-
Abdulkader Aljandali, Motasam Tatahi
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MCN, The American Journal of Maternal/Child Nursing, 1993
Abstract In many biological and medical situations a variable is observed sequentially over a period of time. The resulting set of observations, ordered with respect to time, is called a time series. For example, if the temperature in a certain place is measured at noon on each day for a year, the resulting set of values is a time ...
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Abstract In many biological and medical situations a variable is observed sequentially over a period of time. The resulting set of observations, ordered with respect to time, is called a time series. For example, if the temperature in a certain place is measured at noon on each day for a year, the resulting set of values is a time ...
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2014
A time series is a sequential collection of data indexed over time. In most cases the data are continuous but they are recorded at a discrete and finite set of equally spaced points. If a time series has N-observations (x 0, x 1, …, x N ), then the time indexed distance between any two successive observations is referred to as the sampling interval ...
Asis Kumar Chattopadhyay +1 more
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A time series is a sequential collection of data indexed over time. In most cases the data are continuous but they are recorded at a discrete and finite set of equally spaced points. If a time series has N-observations (x 0, x 1, …, x N ), then the time indexed distance between any two successive observations is referred to as the sampling interval ...
Asis Kumar Chattopadhyay +1 more
+4 more sources
nonlinear time series analysis [PDF]
, 2008 Since the early 1980s, there has been a growing interest in stochastic nonlinear dynamical systems of the form, where is a zero mean, covariance stationary process, is the conditional volatility, and is an independent and identically distributed noise process.
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2009
Abstract This article discusses time-series methods such as simple time-series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). It specifically presents a brief history of time-series analysis before moving to a review of the basic time-series model.
Jon C. W. Pevehouse, Jason D. Brozek
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Abstract This article discusses time-series methods such as simple time-series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). It specifically presents a brief history of time-series analysis before moving to a review of the basic time-series model.
Jon C. W. Pevehouse, Jason D. Brozek
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Technometrics, 1997
Preface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman ...
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Preface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman ...
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2004
Time series analysis is the technique used to study observations that are measured over time. Examples include natural phenomena (temperature, humidity, wind speed) and business variables (price of commodities, stock market indices) that are measured at regular intervals (hourly, daily).
Richard M. Heiberger, Burt Holland
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Time series analysis is the technique used to study observations that are measured over time. Examples include natural phenomena (temperature, humidity, wind speed) and business variables (price of commodities, stock market indices) that are measured at regular intervals (hourly, daily).
Richard M. Heiberger, Burt Holland
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