Autoregressive Process

Any process or model that uses past data to predict future data. Technical analysis, for example, is an autoregressive process. See also: Forecasting.

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According to these authors, whenever residual dependence is observed, this correlation must be modeled by an autoregressive process. Different results were found by Ribeiro et al.

Drying kinetics of jabuticaba pulp by regression models/Cinetica de secagem de polpa de jabuticaba por modelos de regressao

From the Durbin-Watson test (DW), the presence of residual autocorrelation was evident for fruit length in the Logistic model and for diameter in the Gompertz model, necessitating the incorporation of the second-order autoregressive process parameters.

The use of the nonlinear models in the growth of pears of 'Shinseiki' cultivar/O uso de modelos nao lineares na descricao do crescimento de frutos de pereira cultivar 'Shinseiki'

Thus, the speech signal is modeled as an autoregressive process of order P, AR(P), according to

Comparative Study between the Discrete-Frequency Kalman Filtering and the Discrete-Time Kalman Filtering with Application in Noise Reduction in Speech Signals

Assuming that output gap is autoregressive process, one period ahead forecast may be upward biased in recession and downward biased in boom.

The Unreliability of Output-Gap Estimates in Real Time

According to Makridakis and Hibon (2000) ARIMA is a combination of 3-parameter model, which consists of Autoregressive process (memory of past events), an integrated process (maintaining and preparing the data fixed and accessible to predict) and moving average (the older the data is the more perfect the prediction will be).

Karachi inter-bank offered rate (KIBOR) forecasting: box-jenkins (ARIMA) testing approach

For a stationary time series [x.sub.t] (t = 1, 2, ..., N), autoregressive process produces the following results:

Multipoint and Multiobjective Optimization of a Centrifugal Compressor Impeller Based on Genetic Algorithm

Results are reported in Section 4 where the retrieval of the true causality network from the analytics of time series from an autoregressive process of p = 100 variables is discussed.

Sparse Causality Network Retrieval from Short Time Series

An autoregressive process will only be stable if the parameters are within a certain range: for example, if there is only one autoregressive parameter then is must fall within the interval of 1less than xt less than 1.

Stochastic Modeling of Solar Flare Duration at Pakistan Atmospheric Region

To eliminate the autoregressive process of the level shift component, the first difference model only depends on the Bernoulli process: [DELTA] [y.sub.t] = [[tau].sub.t] - [[tau].sub.t-1= + [c.sub.t] - [c.sub.t-1] = [c.sub.t] - [c.sub.t-1] + [[delta].sub.t], and passing to the state space form, the measurement and transition equations are obtained, respectively: [DELTA] [y.sub.t] = [c.sub.t] - [c.sub.t-1] + [[delta].sub.t], [c.sub.t] = [phi] [[c.sub.t-1] + [e.sub.t].

Application of a short memory model with random level shifts to the volatility of Latin American stock market returns

We propose a more robust technique to estimate the effective sample size for the case of an autoregressive process of order 1 (AR1), a suitable hypothesis for many time series in meteorology and climate sciences.

Dependencies in statistical hypothesis tests for climate time series

To jointly estimate the autoregressive process and its parameters from noisy observations, one of the authors of this paper has recently proposed dual Kalman filters based structure [11, 20].

Estimation of FBMC/OQAM fading channels using dual Kalman filters

From the previous subsection, we know that the stochastic process is a kth order Gauss-Markov process and, consequently, an autoregressive process. Under a system theory point of view, an autoregressive process is such that the system has only poles, that is,

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis