# Moving Transform¶

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

## Signals¶

### Inputs¶

**Time series**Time series as output by

*As Timeseries*widget.

### 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.

- 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).

## See also¶

## Example¶

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

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