Integrated processes and the discrete cosine transform

2001 ◽  
Vol 38 (A) ◽  
pp. 105-121 ◽  
Author(s):  
Robert B. Davies

A time-series consisting of white noise plus Brownian motion sampled at equal intervals of time is exactly orthogonalized by a discrete cosine transform (DCT-II). This paper explores the properties of a version of spectral analysis based on the discrete cosine transform and its use in distinguishing between a stationary time-series and an integrated (unit root) time-series.

2001 ◽  
Vol 38 (A) ◽  
pp. 105-121
Author(s):  
Robert B. Davies

A time-series consisting of white noise plus Brownian motion sampled at equal intervals of time is exactly orthogonalized by a discrete cosine transform (DCT-II). This paper explores the properties of a version of spectral analysis based on the discrete cosine transform and its use in distinguishing between a stationary time-series and an integrated (unit root) time-series.


Author(s):  
Stefan Birr ◽  
Stanislav Volgushev ◽  
Tobias Kley ◽  
Holger Dette ◽  
Marc Hallin

2021 ◽  
Vol 60 (1) ◽  
pp. 1767-1775
Author(s):  
Mohammad Reza Mahmoudi ◽  
Dumitru Baleanu ◽  
Sultan Noman Qasem ◽  
Amirhosein Mosavi ◽  
Shahab S. Band

1986 ◽  
Vol 23 (A) ◽  
pp. 41-54 ◽  
Author(s):  
Emanuel Parzen

An approach to time series model identification is described which involves the simultaneous use of frequency, time and quantile domain algorithms; the approach is called quantile spectral analysis. It proposes a framework to integrate the analysis of long-memory (non-stationary) time series with the analysis of short-memory (stationary) time series.


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