Singular Spectrum Analysis (SSA) for Time Series

This Python class provides an implementation of the Singular Spectrum Analysis (SSA) for time series gap filling and forecasting.

Features:

• Time series decomposition
• Gap filling in time series data
• Forecasting future values

Usage:

1. Initialization: Initialize the SSA object with your time series data.

   ssa = SSA(your_time_series)

2. Embedding: Embed the time series data.

   ssa.embed()

3. Decomposition: Decompose the time series into its singular values.

   ssa.decompose()

4. Gap Filling: If your time series has missing values, you can fill them using:

   ssa.forecast(steps_ahead=0)

5. Forecasting: Predict future values of your time series by specifying the number of steps ahead you want to forecast.

   ssa.forecast(steps_ahead=number_of_steps)