Multivariate time series symbolization

Here we domonstrate how to use fABBA to symbolize multivariate (same applies to multiple univariate time series) with consistent symbols. After downloading the UEA time series dataset in corresponding folder, you can run JABBA following the example below:

import os
from import arff
from fABBA import JABBA
import matplotlib.pyplot as plt
import numpy as np

_dir = 'data/UEA2018' # your data file location

def preprocess(data):
    time_series = list()
    for ii in data[0]:
        database = list()
        for i in ii[0]:
    return np.nan_to_num(np.array(time_series))

filename = 'BasicMotions'
num= 10
data = arff.loadarff(os.path.join(_dir, os.path.join(filename, filename+'_TRAIN.arff')))
multivariate_ts = preprocess(data)

mts =((multivariate_ts[num].T - multivariate_ts[num].T.mean(axis=0)) /multivariate_ts[num].T.std(axis=0)).T

jabba1 = JABBA(tol=0.0002, verbose=1)
symbols_series = jabba1.fit_transform(mts)
reconstruction = jabba1.inverse_transform(symbols_series)

jabba2 = JABBA(tol=0.0002, init='k-means', k=jabba1.parameters.centers.shape[0], verbose=0)
symbols_series = jabba2.fit_transform(mts)
reconstruction_ABBA = jabba2.inverse_transform(symbols_series)

fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(18, 5))

for i in range(2):
    for j in range(3):
        ax[i,j].plot(mts[i*3 + j], c='yellowgreen', linewidth=5,label='time series')
        ax[i,j].plot(reconstruction_ABBA[i*3 + j], c='blue', linewidth=5, alpha=0.3,label='reconstruction - J-ABBA')
        ax[i,j].plot(reconstruction[i*3 + j], c='purple', linewidth=5, alpha=0.3,label='reconstruction - J-fABBA')

        ax[i,j].set_title('dimension '+str(i*3 + j))

plt.legend(loc='lower right', bbox_to_anchor=[-0.5, -0.5], framealpha=0.45)

fABBA enable symbolic approximation of multidimentioanl array. Users simply can recontruct the symbols into original shape via recast_shape .

from fABBA import JABBA
import numpy as np
mts = np.random.randn(10, 20, 30) # 6000 time series values

jabba = JABBA(tol=0.01, alpha=0.01, verbose=1)
symbols = jabba.fit_transform(mts)
reconst = jabba.inverse_transform(symbols) # convert into array
reconst_same_shape = jabba.recast_shape(reconst) # recast into original shape
np.linalg.norm((mts - reconst_same_shape).reshape(-1,[1:])), 'fro')

If one would like to ensure the recast_shape for shape reconstruction, the input to fit_transform must be numpy.ndarray.

Regarding the transformation of out-of-sample data, use

mts = np.random.randn(20, 20, 30) # new 6000 time series values
symbols_trans, start_set = jabba.transform(mts) # Perform transform with fitted model
reconst = jabba.inverse_transform(symbols_trans, start_set)
np.linalg.norm((mts - reconst_same_shape).reshape(-1,[1:])), 'fro')

You can also load dataset via loadData:

from fABBA import loadData
train, test = loadData(name='Beef')
# Then perform JABBA
jabba = JABBA(tol=0.0002, verbose=1)
symbols_series = jabba.fit_transform(train[0])
reconstruction = jabba.inverse_transform(symbols_series)


function loadData() is a lightweight API for time series dataset loading, which only supports part of data in UEA or UCR Archive, please refer to the document for full use detail. JABBA is used to process multiple time series as well as multivariate time series, so the input should be ensured to be 2-dimensional, for example, when loading the UCI dataset, e.g., Beef, use symbols = jabba.fit_transform(train) , when loading UEA dataset, e.g., BasicMotions, use symbols = jabba.fit_transform(train[0]) . For details, we refer to UCR/UEA time series dataset. Functionality of loadData() currently supports datasets: (1) UEA Archive: ‘AtrialFibrillation’, ‘BasicMotions’, ‘BasicMotions’, ‘CharacterTrajectories’, ‘LSST’, ‘Epilepsy’, ‘NATOPS’, ‘UWaveGestureLibrary’, ‘JapaneseVowels’; (2) UCR Archive: ‘Beef’.