Apply rolling window functions to the time series. Use this widget to get a series’ mean.
- Time series: The input time series with the added series’ transformations.
In this widget, you define what aggregation functions to run over the time series and with what window sizes.
- Define a new transformation.
- Remove the selected transformation.
- Time series you want to run the transformation over.
- Desired window size.
- Aggregation function to aggregate the values in the window with. Options are: mean, sum, max, min, median, mode, standard deviation, variance, product, linearly-weighted moving average, exponential moving average, harmonic mean, geometric mean, non-zero count, cumulative sum, and cumulative product.
- Select Non-overlapping windows options if you don’t want the moving windows to overlap but instead be placed side-to-side with zero intersection.
- In the case of non-overlapping windows, define the fixed window width(overrides and widths set in (4).
To get a 5-day moving average, we can use a rolling window with mean aggregation.
To integrate time series’ differences from Difference widget, use Cumulative sum aggregation over a window wide enough to grasp the whole series.