Timeseries Analysis with GPs

We use time-series decomposition to try to explain/analyze the quality of our FTGPs. Time-series decomposition helps us break down a time-series dataset into three main parts:

1. Trend: The trend component represents the long-term movement in the data, representing the underlying pattern.

2. Seasonality: The seasonality component represents the repeating, short-term fluctuations caused by factors like seasons or cycles.

3. Residual (Noise): The residual component represents random variability that remains after removing the trend and seasonality.

By separating these components, we can gain insights into the behavior of the data and make better forecasts.