Results 281 to 290 of about 1,758,719 (322)
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Physics Letters A, 1995
Abstract We introduce a technique to characterize and measure predictability in time series. The technique allows one to formulate precisely a notion of the predictable component of given time series. We illustrate our method for both numerical and experimental time series data.
Celso Grebogi +3 more
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Abstract We introduce a technique to characterize and measure predictability in time series. The technique allows one to formulate precisely a notion of the predictable component of given time series. We illustrate our method for both numerical and experimental time series data.
Celso Grebogi +3 more
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1993
A classic statement of the problem of predicting stationary time series x(t) is as follows [6.1, 6.2]. Suppose that a stationary random time series x(t) is defined on time axis t ∈ [∞,+∞]. To simplify the discussion, let us assume that the mean value of the process is zero: $${\rm{E}}\,x\left( t \right) = 0.$$
J. Santana, R. Vilela Mendes
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A classic statement of the problem of predicting stationary time series x(t) is as follows [6.1, 6.2]. Suppose that a stationary random time series x(t) is defined on time axis t ∈ [∞,+∞]. To simplify the discussion, let us assume that the mean value of the process is zero: $${\rm{E}}\,x\left( t \right) = 0.$$
J. Santana, R. Vilela Mendes
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On count time series prediction
Journal of Statistical Computation and Simulation, 2013We consider the problem of assessing prediction for count time series based on either the Poisson distribution or the negative binomial distribution. By a suitable parametrization we employ both distributions with the same mean. We regress the mean on its past values and the values of the response and after obtaining consistent estimators of the ...
Christou, V. +3 more
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Fuzzy prediction of time series [PDF]
[1992 Proceedings] IEEE International Conference on Fuzzy Systems, 2003 An approach to time series extrapolation based on fuzzy control is described. The standard exponential averaging scheme is inflexible in that it gives a fixed weight to past history, thus ignoring transient phases in system dynamics. A modification to the scheme where the control parameter of the averaging scheme is dynamically adjusted by a simple ...
S. Keshav, P.S. Khedkar
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Predicting a Multitude of Time Series
Journal of the American Statistical Association, 1981Abstract Principles for constructing estimators of the Stein type (shrinkage estimators) are discussed in general terms, with emphasis on underlying assumptions. The problem of parameter estimation and prediction for multiple time series is examined with these principles in mind, particularly for the case in which the number of time series is large and
William E. Wecker, Ronald A. Thisted
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Asset Pricing: Time-Series Predictability
SSRN Electronic Journal, 2021Asset returns change with fundamentals and other factors, such as technical information and sentiment over time. In modeling time-varying expected returns, this article focuses on the out-of-sample predictability of the aggregate stock market return via extensions of the conventional predictive regression approach.
David E. Rapach +2 more
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On the Error of Prediction of a Time Series
Biometrika, 1972Abstract : Parametric and nonparametric procedures for the prediction of a time series are discussed. In each case the increase in the mean squared error of prediction over its minimum level due to the use of estimated spectra is assessed. The fitting of simple parametric models as approximations is also discussed. (Author)
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1983
The covariance matrix of a stacked data vector (y1′, y2′, ... yn′)′ of a meanzero weakly stationary process {yt} has a special structure: A submatrix Λ0 =Ey1y1′ is located along the main diagonal, the matrix \({\Lambda _\ell } = E{y_{\ell + 1}}{y'_1}\) along the \( \ell\)-th diagonal below the main diagonal, and \({\Lambda _{ - \ell }} = E{y_1}{y'_ ...
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The covariance matrix of a stacked data vector (y1′, y2′, ... yn′)′ of a meanzero weakly stationary process {yt} has a special structure: A submatrix Λ0 =Ey1y1′ is located along the main diagonal, the matrix \({\Lambda _\ell } = E{y_{\ell + 1}}{y'_1}\) along the \( \ell\)-th diagonal below the main diagonal, and \({\Lambda _{ - \ell }} = E{y_1}{y'_ ...
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2002
The problem of the prediction of time series belongs to the most important problems of the statistical inference of time series. There are many approaches to these problems, possibly the best known is that based on the Box-Jenkins methodology of modeling time series by using ARMA and ARIMA models, another approach is based on modeling time series by ...
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The problem of the prediction of time series belongs to the most important problems of the statistical inference of time series. There are many approaches to these problems, possibly the best known is that based on the Box-Jenkins methodology of modeling time series by using ARMA and ARIMA models, another approach is based on modeling time series by ...
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Time series — information and prediction
Biological Cybernetics, 1990A time series Y t can be transformed into another time series V t by means of a linear transformation. Should the matrix of that transformation have an inverse, the pair (Y t, V t) is called invertible. Based on the decomposition procedure for stationary time series introduced in a previous paper it is shown that a sufficient condition for the ...
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