SAX

SAX

class SAX(word_size=8, alphabet_size=5, frame_size=0)[source]

Symbolic Aggregate approXimation Transformer (SAX).

SAX [2] is a dimensionality reduction technique that z-normalises a time series, applies Piecewise Aggregate Approximation (PAA) [1], and bins the mean of each PAA frame to a discrete value, resulting in a SAX word.

This implementation offers two variants:

1) the original, which takes the desired number of frames and can set the frame size to a fraction to support cases where the time series cannot be divided into the frames equally. 2) a variant that takes the desired frame size and can decrease the frame size of the last frame to support cases where the time series is not evenly divisible into frames.

Parameters:

word_sizeint, optional (default=8, greater equal 1 if frame_size=0)

length of transformed time series. Ignored if frame_size is set.

alphabet_sizeint, optional (default=5, greater equal 2)

number of discrete values transformed time series is binned to.

frame_sizeint, optional (default=0, greater equal 0)

length of the frames over which the mean is taken. Overrides frames if > 0.

Attributes:

is_fitted

Whether fit has been called.

References

[1]

Keogh, E., Chakrabarti, K., Pazzani, M., and Mehrotra, S. Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowledge and Information Systems 3, 263-286 (2001). https://doi.org/10.1007/PL00011669

[2]

Lin, J., Keogh, E., Wei, L., and Lonardi, S. Experiencing SAX: A Novel Symbolic Representation of Time Series. Data Mining and Knowledge Discovery 15, 107-144 (2007). https://doi.org/10.1007/s10618-007-0064-z

Examples

>>> from numpy import arange
>>> from sktime.transformations.sax import SAX

>>> X = arange(10)
>>> sax = SAX(word_size=3, alphabet_size=5)
>>> sax.fit_transform(X)
array([0, 2, 4])
>>> sax = SAX(frame_size=2, alphabet_size=5)
array([0, 1, 2, 3, 4])

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit transformer to X, optionally to y.
fit_transform(X[, y])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.