Extensible ABBA

We also provide other clustering based ABBA methods for extesion, it is easy to use with the support of scikit-learn tools. For users who want to develop their own clustering based ABBA, this tutorial is quite useful, in particular to research comparsion. The user guidance is as follows

import numpy as np
from sklearn.cluster import KMeans
from fABBA import ABBAbase

ts = [np.sin(0.05*i) for i in range(1000)]         # original time series
#  specifies 5 symbols using kmeans clustering
kmeans = KMeans(n_clusters=5, random_state=0, init='k-means++', verbose=0)
abba = ABBAbase(tol=0.1, scl=1, clustering=kmeans)
string = abba.fit_transform(ts)                    # string representation of the time series
print(string)                                      # prints BbAaAaAaAaAaAaAaC
inverse_ts = abba.inverse_transform(string)        # reconstruction

Note fABBA software package is not limited to fABBA, you can directly call ABBA with:

from fABBA import ABBA
abba = ABBA(tol=0.1, scl=1, k=5, verbose=0)
string = abba.fit_transform(ts)

it will output:

Compression: Reduced series of length 1000 to 17 segments. Digitization: Reduced 17 pieces to 5 symbols.