SAST is a novel shapelet-based time series classification method inspired by the core object recognition capability of human brain. SAST is more accurate than STC while being more scalable.

SASTEN is an ensemble of 3 SAST models. SASTEN is more accurate than SAST and more scalable than STC.

SASTEN-A is an ensemble of 3 approximated SAST models. The approximation is done by considering only a subset of the subsequences in the dataset.

STC-k is a shapelet transform classifier which generate shapelet candidates from at most k reference time series per class. If k is a float, then k x n_c instances are used per class, where n_c is the total number of instances in class c.

Updated version with more results

• All models results
• SAST results
• STC-k results
• Approximated SAST results: SAST-A
• Ensemble of approximated SAST results: SASTEN-A
• Ensemble of SAST results: SASTEN
• Execution time regarding the number of series
• Execution time regarding series length

• Regarding the length of time series

• Regarding the number of time series in the dataset

import numpy as np
from sast.utils import *
from sast.sast import *
from sklearn.linear_model import RidgeClassifierCV

clf = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))
sast_ridge = SAST(cand_length_list=np.arange(min_shp_length, max_shp_length+1),
		          nb_inst_per_class=nb_inst_per_class, 
		          random_state=None, classifier=clf)

sast_ridge.fit(X_train, y_train)

prediction = sast_ridge.predict(X_test)

• numpy == 1.18.5
• numba == 0.50.1
• scikit-learn == 0.23.1
• sktime == 0.5.3