What is singular spectrum analysis?

Singular Spectrum Analysis (SSA) is a data analysis technique that is used to extract the underlying trends and patterns from time-series data. It was developed in the early 1990s by N. Golyandina, V. Nekrutkin, and A. Zhigljavsky.

The SSA algorithm decomposes the original time series into a finite number of basic components or oscillations called singular vectors or eigenvectors. These components are ordered according to the amount of variance they represent, and the sum of the components represents the original time series.

SSA has several advantages over other time-series analysis techniques. One of the main advantages is that it can effectively deal with short and noisy data sets, which are difficult to analyze using traditional methods such as Fourier analysis or wavelet transforms.

SSA can be used for a variety of applications, such as weather forecasting, financial market analysis, speech analysis, image processing, and biomedical data analysis. It is particularly useful in detecting and analyzing periodicities, trends, and anomalies in time-series data.

Overall, SSA is a powerful tool for exploring and analyzing complex time-series data, and it has become increasingly popular in recent years due to its effectiveness, simplicity, and versatility.