# Interpolate¶

Induce missing values in the time series by interpolation.

## Signals¶

### Inputs¶

**Time series**Time series as output by

*As Timeseries*widget.

### Outputs¶

**Time series**The input time series but preset with chosen default interpolation method for when algorithms require interpolated time series (without missing values).

**Interpolated time series**The input time series with any missing values interpolated according to chosen interpolation method.

## Description¶

Most time series algorithms assume, you don’t have any missing values in your data. In this widget, you can chose the interpolation method to impute the missing values with. By default, it’s linear interpolation (fast and reasonable default).

- Interpolation type. You can select one of linear, cubic spline, nearest, or mean interpolation.
**Linear**interpolation replaces missing values with linearly-spaced values between the two nearest defined data points.**Spline**interpolation fits a cubic polynomial to the points around missing values. This is a painfully slow method that usually gives best results.**Nearest**interpolation replaces missing values with the previous defined value.**Mean**interpolation replaces missing values with the series mean. - Select
**Multi-variate interpolation**to interpolate the whole series table as a two-dimensional plane instead of as separate single-dimensional series.

Note

Missing values on the series’ end points (head and tail)
are always interpolated using *nearest* method.

Note

Unless the interpolation method is set to *nearest*, discrete
time series (i.e. sequences) are always imputed with
the series *mode* (most frequent value).