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2004
In this chapter we will deal with classic, linear time series analysis. At first we will define the general linear process.
Jürgen Franke +2 more
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In this chapter we will deal with classic, linear time series analysis. At first we will define the general linear process.
Jürgen Franke +2 more
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Applied Mechanics and Materials, 2013
It is often the case that managers and social scientists are called to deal with time series. Time series analysis usually involves a study of the components of the time series and finding models that permit statistical inferences and predictions. ARIMA models are, in theory, the most general class of models for forecasting a time series.
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It is often the case that managers and social scientists are called to deal with time series. Time series analysis usually involves a study of the components of the time series and finding models that permit statistical inferences and predictions. ARIMA models are, in theory, the most general class of models for forecasting a time series.
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An enhanced ARIMA model for EEG classification
IEEE/WIC/ACM International Conference on Web Intelligence, 2021Yan Liu +5 more
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Stationarity, VARMA, and ARIMA Models
2019Statistically speaking, a time seriesy is a finite set of values {y1…, yn} taken by certain k-dimensional random vectors {Y1…, Yn}. The proper framework in which to study time series is that of stochastic processes.
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The Order of Differencing in ARIMA Models
Journal of the American Statistical Association, 1984Abstract A Lagrange multiplier test is derived for testing for the order of differencing in an autoregressive integrated moving average (ARIMA) model. The procedure is illustrated with an example.
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1990
Kann eine vorliegende Zeitreihe x1, x2,…,xT, als Realisation eines schwach stationaren stochastischen Prozesses angesehen werden, dann ist es entweder ein AR(p)-Prozess, ein MA(q)-Prozess oder eine Mischung aus einem schwach stationaren autoregressiven Prozess Xt p-ter Ordnung des (mittelwertbereinigten) Outputs mit einem gleitenden ...
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Kann eine vorliegende Zeitreihe x1, x2,…,xT, als Realisation eines schwach stationaren stochastischen Prozesses angesehen werden, dann ist es entweder ein AR(p)-Prozess, ein MA(q)-Prozess oder eine Mischung aus einem schwach stationaren autoregressiven Prozess Xt p-ter Ordnung des (mittelwertbereinigten) Outputs mit einem gleitenden ...
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ARIMA Model for Stock Market Prediction
2022 8th International Conference on Computer Technology Applications, 2022openaire +2 more sources
2014
Bisher wurden in diesem Kapitel die linearen Modelle dazu benutzt, ausschlieslich stationare Prozesse zu modellieren und prognostizieren.
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Bisher wurden in diesem Kapitel die linearen Modelle dazu benutzt, ausschlieslich stationare Prozesse zu modellieren und prognostizieren.
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