SSALib: a Python Library for Time Series Decomposition using Singular Spectrum Analysis

Singular Spectrum Analysis (SSA) is a method developed in the 1980s for analyzing and decomposing time series.Using time-delayed trajectories or covariance matrices, SSA takes advantage of temporal dependencies to identify structured components such as trends and cycles.Time series decomposition has various applications, including denoising, filtering, signal modeling, interpolation (or gap filling), and extrapolation (or forecasting).The Singular Spectrum Analysis Library (SSALib) is a Python package that simplifies SSA implementation and visualization for the decomposition of univariate time series, featuring component significance testing.