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SSRN Electronic Journal, 2006 Any series of observations ordered along a single dimension, such as time, may be thought of as a time series. The emphasis in time series analysis is on studying the dependence among observations at different points in time. What distinguishes time series analysis from general multivariate analysis is precisely the temporal order imposed on the ...
Francis X. Diebold +2 more
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American Journal of Orthodontics and Dentofacial Orthopedics, 2022
This article describes a simple method of applying a time series analysis to sample data sets using a free and open statistical software program, Language R.Records of new patients who visited 2 different university-affiliated orthodontic departments in 2 different countries were collected.
Richard E. Donatelli +3 more
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This article describes a simple method of applying a time series analysis to sample data sets using a free and open statistical software program, Language R.Records of new patients who visited 2 different university-affiliated orthodontic departments in 2 different countries were collected.
Richard E. Donatelli +3 more
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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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2017
In this chapter, we describe three different synthetic datasets that we considered to evaluate the performance of the reviewed recurrent neural network architectures in a controlled environment. The generative models of the synthetic time series are the Mackey–Glass system, NARMA, and multiple superimposed oscillators.Those are benchmark tasks commonly
Bianchi, Filippo Maria +4 more
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In this chapter, we describe three different synthetic datasets that we considered to evaluate the performance of the reviewed recurrent neural network architectures in a controlled environment. The generative models of the synthetic time series are the Mackey–Glass system, NARMA, and multiple superimposed oscillators.Those are benchmark tasks commonly
Bianchi, Filippo Maria +4 more
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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-
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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-
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Time Series: State Space Methods
2015State space modeling provides a unified methodology for treating a wide range of problems in time series analysis. The Kalman filter and its related methods have become key tools in the analysis of time series in economics, finance, and in many other fields as well.
Koopman, Siem J. +1 more
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1987
The concept of a stationary time series was, apparently, formalized by Khintchine in 1932. An infinite sequence y(t), t = 0, + 1, …, of random variables is called stationary if the joint probability law of y(t1), y(t2), …, y(tn) is the same as that of y(t1+t) …, y(tn +1) for any integers, t1, t2, …, tn, t and any n.
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The concept of a stationary time series was, apparently, formalized by Khintchine in 1932. An infinite sequence y(t), t = 0, + 1, …, of random variables is called stationary if the joint probability law of y(t1), y(t2), …, y(tn) is the same as that of y(t1+t) …, y(tn +1) for any integers, t1, t2, …, tn, t and any n.
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Construction Time Series Forecasting Using Univariate Time Series Models
2023Mohsen Shahandashti +3 more
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