Utils for filling missing values#

darts.utils.missing_values.extract_subseries(series, min_gap_size=1, mode='all')[source]#

Partitions the series into a sequence of sub-series by using significant gaps of missing values

Parameters:

  • series (TimeSeries) – The TimeSeries to partition into sub-series

  • min_gap_size (Optional[int]) – The minimum number of contiguous missing values to consider a gap as significant. Defaults to 1.

  • mode (str) – Only for multivariate TimeSeries. The definition of a gap; presence of a NaN in any column (“any”) or NaNs in all the columns (“all”) for a given timestamp. Defaults to “all”.

Returns:

A list of TimeSeries, sub-series without significant gaps of missing values

Return type:

subseries

See also

TimeSeries.gaps

return the gaps in the TimeSeries

darts.utils.missing_values.fill_missing_values(series, fill='auto', **interpolate_kwargs)[source]#

Fills missing values in the provided time series

Parameters:

  • series (TimeSeries) – The time series for which to fill missing values

  • fill (Union[str, float]) – The value used to replace the missing values. If set to ‘auto’, will auto-fill missing values using the pandas.Dataframe.interpolate() method.

  • interpolate_kwargs – Keyword arguments for pandas.Dataframe.interpolate(), only used when fit is set to ‘auto’. See the documentation for the list of supported parameters.

Returns:

A new TimeSeries with all missing values filled according to the rules above.

Return type:

TimeSeries

darts.utils.missing_values.missing_values_ratio(series)[source]#

Computes the ratio of missing values

Parameters:

series (TimeSeries) – The time series to compute ratio on

Returns:

The ratio of missing values

Return type:

float