Trend periodic non-stationary processes (or trend cyclostationary processes) are a type of cyclostationary process that exhibits both periodic behavior and a statistical trend. The trend can be linear or nonlinear, and it can result from systematic changes in the data over time. A cyclostationary process can be formed by removing the trend component. This approach is utilized in the analysis of the trend-stationary process.
In data analysis classification of periodic data into stationary-periodic, trend-periodic and stochastic-periodic time series is achieved by means of phase dispersion minimization (PDM) test, which is a method for identifying periodicity.[1]
- ^ Poghosyan, Arnak; Harutyunyan, Ashot; Grigoryan, Naira; Pang, Clement; Oganesyan, George; Ghazaryan, Sirak; Hovhannisyan, Narek (25 February 2021). "An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning". Sensors. 21 (1590): 1590. Bibcode:2021Senso..21.1590P. doi:10.3390/s21051590. PMC 7956489. PMID 33668753.