New advances in science and technology have made it possible to rigorously study our nature and environment. Most data sets are collections of measurements obtained over time, referred to time series. For example, a collection of satellite imagery or light curves acquired by telescopes within certain time periods, etc.
Image Credit: E. Ghaderpour
These time series contain many components of interests or signals which we need to find out what they are in order to understand the physics behind the phenomena. Decomposing a time series into the time-frequency domain is one powerful way that can help us to estimate what components the time series has. The least-squares wavelet analysis is a novel method proposed for this purpose. It does not necessarily require equally spaced time series, and it can consider the observational uncertainties. This method has shown promising results in analyzing various types of time series.