Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149515 | Journal of Statistical Planning and Inference | 2010 | 12 Pages |
The linear chirp process is an important class of time series for which the instantaneous frequency changes linearly in time. Linear chirps have been used extensively to model a variety of physical signals such as radar, sonar, and whale clicks (see Altes, 1990, Kay and Boudreaux-Bartels, 1985 and et al., 2003). We introduce the stochastic linear chirp model and then define the generalized linear chirp (GLC) process as a special case of the G-stationary process studied by Jiang et al. (2006) to model data with time-varying frequencies. We then define GLC(p,q) processes and show that the relationship between stochastic linear chirp processes and GLC(p,q) processes is analogous to that between harmonic and ARMA models. The new methods are then applied to both simulated and actual data sets.