Astronomical Time Series Analysis

January 10, 2019

  • Technology Stack – Python: Numpy, Scipy, Astropy, astroML, Tkinter
  • The project demonstrates Spectral Analysis and Forecasting performed on Time Series obtained from Astronomical Observations
  • The project employs the use of various Periodogram-based techniques like Fourier Transform, Lomb - Scargle Method, to estimate the Power Spectral Density at various frequencies.
  • The accurate forecasting of the data was done by estimating the various parameters of an ARIMA model, by the analysis of the Auto-Correlation Function (ACF) and the Partial Auto-Correlation Function (PACF) of the data.
  • The above-mentioned methods were employed on both evenly-sampled data (LIGO) as well as unevenly-sampled data (LINEAR)