detection and prediction of spectrally correlated processes withnon-parallel support lines

In this work, we try to make inference about the detection and prediction ofspectrally correlated processes with non-parallel support lines. the loeve bi-frequencyspectrum of these processes is supported by some not-parallel lines. first, the distributionsof the discrete fourier transform (dft) and the periodogram of these processesare explored. then, the spectral ciphering approach is used to detect and to estimatethe spectral supports. finally, the prediction problem of such processes is considered.the validity of theoretical results is studied using simulated datasets.