scholarly journals Generalised cepstral models for the spectrum of vector time series

2020 ◽  
Vol 14 (1) ◽  
pp. 605-631
Author(s):  
Maddalena Cavicchioli
Keyword(s):  
Time Series ◽  
Vector Time Series

A Radiated-Noise Simulator for Underwater Target Based on Vector Time Series

2014 ◽  
Vol 644-650 ◽  
pp. 4023-4026
Author(s):  
Yang Ju ◽  
Xin Yong Wang
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Time Series Model ◽  
Experimental Results ◽  
Small Probability ◽  
Radiated Noise ◽  
True Value ◽  
Underwater Target ◽  
Vector Time Series

The vector time series model for simulating the underwater target radiated-noise is developed in this paper. Experimental results show that the true value lying outside the confidence interval would be a small probability event.

scholarly journals Justification of Wold’s Theorem and the Unbiasedness of a Stable Vector Autoregressive Time Series Model Forecasts

2017 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
Iberedem A. Iwok
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Autoregressive Process ◽  
Time Series Model ◽  
Average Process ◽  
Moving Average Process ◽  
Autoregressive Time Series ◽  
Vector Autoregressive ◽  
Vector Autoregressive Process ◽  
Vector Time Series

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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