# Moving Transform

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

**Inputs**

- Time series: Time series as output by As Timeseries widget.

**Outputs**

- 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).

## Example

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.