Recursive search-based identification algorithms for the exponential autoregressive time series model with coloured noise

In this work, the multivariate analogue to the univariate Wold’s theorem for a purely non-deterministic stable vector time series process was presented and justified using the method of undetermined coefficients. By this method, a finite vector autoregressive process of order [] was represented as an infinite vector moving average () process which was found to be the same as the Wold’s representation. Thus, obtaining the properties of a process is equivalent to obtaining the properties of an infinite process. The proof of the unbiasedness of forecasts followed immediately based on the fact that a stable VAR process can be represented as an infinite VEMA process.

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A Flexible Univariate Autoregressive Time‐Series Model for Dispersed Count Data

Journal of Time Series Analysis ◽

10.1111/jtsa.12516 ◽

2020 ◽

Vol 41 (3) ◽

pp. 436-453 ◽

Cited By ~ 1

Author(s):

Kimberly F. Sellers ◽

Stephen J. Peng ◽

Ali Arab

Keyword(s):

Time Series ◽

Count Data ◽

Time Series Model ◽

Autoregressive Time Series

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Prediction of Rainfall of Allahabad District by the Development of Autoregressive Time Series Model

International Journal of Current Microbiology and Applied Sciences ◽

10.20546/ijcmas.2018.704.170 ◽

2018 ◽

Vol 7 (04) ◽

pp. 1516-1522

Author(s):

K.N. Singh ◽

A. Dalai ◽

M.P. Kaiwart ◽

R.R. Mohanty Mohanty ◽

B. Kumar

Keyword(s):

Time Series ◽

Time Series Model ◽

Autoregressive Time Series

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A new autoregressive time series model in exponential variables (NEAR(1))

Advances in Applied Probability ◽

10.2307/1426975 ◽

1981 ◽

Vol 13 (4) ◽

pp. 826-845 ◽

Cited By ~ 78

Author(s):

A. J. Lawrance ◽

P. A. W. Lewis

Keyword(s):

Time Series ◽

Autoregressive Model ◽

Marginal Distribution ◽

Sample Path ◽

Cross Coupling ◽

Time Series Model ◽

Autoregressive Time Series ◽

First Order ◽

Joint Distributions ◽

Two Parameter

A new time series model for exponential variables having first-order autoregressive structure is presented. Unlike the recently studied standard autoregressive model in exponential variables (ear(1)), runs of constantly scaled values are avoidable, and the two parameter structure allows some adjustment of directional effects in sample path behaviour. The model is further developed by the use of cross-coupling and antithetic ideas to allow negative dependency. Joint distributions and autocorrelations are investigated. A transformed version of the model has a uniform marginal distribution and its correlation and regression structures are also obtained. Estimation aspects of the models are briefly considered.

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