Time delay embedding
The basic idea behind time delay embedding is to embed time in terms of recent observations. In Chapter 5, Time Series Forecasting as Regression, we discussed including previous observations of a time series as lags (Figure 5.6 under the subsection Time delay embedding).
However, there are a few more ways to capture recent and seasonal information using this concept.
- Lags
- Rolling window aggregations
- Seasonal rolling window aggregations
- Exponentially weighted moving averages
Let’s take a look.
Lags or backshift
Let’s assume we have a time series with time steps, YL. Consider that we are at time T and that we have a time series where the length of history is L. So our time series will have yT as the latest observation in the time series, and then yT-1, yT-2, and so on as we move back in time. So lags, as explained in Chapter 5, Time Series Forecasting as Regression, are features that include the previous...
