Transformer
TimeBinner
Turns time series/panel data into tabular data based on intervals.
This estimator converts nested pandas dataframe containing time-series/panel data with numpy arrays or pandas Series in dataframe cells into a tabular pandas dataframe with only primitives in cells. The primitives are calculated based on Intervals defined by the IntervalIndex and aggregated by aggfunc.
This is useful for transforming time-series/panel data into a format that is accepted by standard validation learning algorithms (as in sklearn).
Quickstart
python
from sktime.transformations.reduce import TimeBinner
estimator = TimeBinner(idx, aggfunc=None)Tags
Capabilities
- Multivariate
- Unequal-length series
- Inverse transform: Not supported
- Missing values: Not supported
- Removes missing values: Not supported
- Equalizes series length: Not supported
Properties
- Input typescitype:transform-input
- Series
- Output typescitype:transform-output
- Primitives
- Label typescitype:transform-labels
- None
- Fit is emptyfit_is_empty
- Yes
- Keeps the time indextransform-returns-same-time-index
- No
- Requires Xrequires_X
- Yes
- Requires yrequires_y
- No
- X and y need the same indexX-y-must-have-same-index
- No
Parameters(2)
- idxpd.IntervalIndex
- IntervalIndex defining intervals considered by aggfunc
- aggfunccallable
- Function used to aggregate the values in intervals. Should have signature 1D -> float and defaults to mean if None