Moving Transform

../_images/moving-transform.png

Apply rolling window functions to the time series. Use this widget to get a series’ mean.

Signals

Inputs

Outputs

  • Time series

    The input time series with added series transformations.

Description

In this widget, you define what aggregation functions to run over the time series and with what window sizes.

../_images/moving-transform-stamped.png
  1. Define a new transformation.
  2. Remove the selected transformation.
  3. Time series you want to run the transformation over.
  4. Desired window size.
  5. 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.
  6. 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.
  7. In the case of non-overlapping windows, define the fixed window width (overrides and widths set in (4).

Example

To get a 5-day moving average, we can use a rolling window with mean aggregation.

../_images/moving-transform-ex1.png

To integrate a differenced time series, use Cumulative sum aggregation over a window wide enough to grasp the whole series.

../_images/moving-transform-ex2.png