Spectral Analysis : Time Series Analysis in Frequency Domain

This paper describes how the frequency domain analysis provides an alternative approach to time domain analysis for a given time series. Spectral and periodogram analysis for a given time series are performed to detect trends and seasonalities in the data. A cross-spectral analysis is used to find causality and co-movements in two different time series. Univariate frequency domain analysis is done using time series of varying nature including simulated white noise process, random walk process, AR (1) process, Wolfer’s Sunspot Data and Box-Jenkins Airlines Data; while bivariate (cross spectral) analysis is done for macroeconomic variables such as Money in Circulation and Inflation