Transformer
Bollinger
Apply Bollinger bands to a time series.
The transformation works for univariate and multivariate timeseries.
Quickstart
python
from sktime.transformations.bollinger import Bollinger
estimator = Bollinger(window, k=1, memory='all')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
- Series
- Label typescitype:transform-labels
- None
- Fit is emptyfit_is_empty
- No
- Keeps the time indextransform-returns-same-time-index
- Yes
- Requires Xrequires_X
- Yes
- Requires yrequires_y
- No
- X and y need the same indexX-y-must-have-same-index
- No
Parameters(3)
- windowint
- The window over which to compute the moving average and the standard deviation.
- k: float, default = 1
- Multiplier to determine how many stds the upper and lower bounds are from the moving average.
- memorystr, optional, default = “all”
how much of previously seen X to remember, for exact reconstruction of inverse.
“all”: estimator remembers all X, inverse is correct for all indices seen
“latest”: estimator only remembers latest X necessary for future
reconstruction. Inverses at any time stamps after fit are correct, but not past time stamps.
“none”: estimator does not remember any X, inverse is direct cumsum
Examples
>>> from sktime.transformations.bollinger import Bollinger
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = Bollinger (window = 12, k = 1)
>>> y_transform = transformer. fit_transform (y)