MUSE¶
-
class
sktime.classification.dictionary_based.
MUSE
(anova=True, bigrams=True, window_inc=2, p_threshold=0.05, use_first_order_differences=True, random_state=None)[source]¶ WEASEL+MUSE (MUltivariate Symbolic Extension) MUSE: implementation of multivariate version of WEASEL, referred to as just MUSE from [1]
- Overview: Input n series length m
WEASEL+MUSE is a multivariate dictionary classifier that builds a bag-of-patterns using SFA for different window lengths and learns a logistic regression classifier on this bag.
- There are these primary parameters:
alphabet_size: alphabet size chi2-threshold: used for feature selection to select best words anova: select best l/2 fourier coefficients other than first ones bigrams: using bigrams of SFA words binning_strategy: the binning strategy used to disctrtize into
SFA words.
- Parameters
anova (boolean, default = True) – If True, the Fourier coefficient selection is done via a one-way ANOVA test. If False, the first Fourier coefficients are selected. Only applicable if labels are given
bigrams (boolean, default = True) – whether to create bigrams of SFA words
window_inc (int, default = 4) –
- WEASEL create a BoP model for each window sizes. This is the
increment used to determine the next window size.
- p_threshold: int, default = 0.05 (disabled by default)
Feature selection is applied based on the chi-squared test. This is the p-value threshold to use for chi-squared test on bag-of-words (lower means more strict). 1 indicates that the test should not be performed.
use_first_order_differences (boolean, default = True) – If set to True will add the first order differences of each dimension to the data.
See also
Notes
..[1] Patrick Schäfer and Ulf Leser, “Multivariate time series classification with WEASEL+MUSE”, in proc 3rd ECML/PKDD Workshop on AALTD}, 2018 https://arxiv.org/abs/1711.11343 Java version https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/ classifiers/multivariate/WEASEL_MUSE.java