This repository provides ABBA, an algorithm for the adaptive symbolic aggregation of time series. The ABBA algorithm consists of two key parts: compression via an adaptive piecewise linear approximation, and digitization via mean-based clustering on the increments and lengths of each piece.

The algorithm uses a scaling parameter scl to control the weighting of the increments and lengths during the clustering. If scl = 0 or scl = np.inf, a one-dimensional clustering algorithm can be used. We use a modified C++ implementation of CKmeans from Ckmeans.1d.dp R package; see Prerequisites. If the C++ implementation is not available or a different scaling parameter is used, then ABBA uses the Kmeans algorithm from the Python package Scikit-learn.

As an example, we consider a synthetic time series and apply ABBA's compression method, which approximates the time series by a sequence of linear segments stitched together. Each segment can be represented by its change in the x-value (len) and change in y-direction (inc).

ABBA's digitization procedure clusters the tuples (len, inc), assigning a unique symbol to each cluster.

The symbolic representation of the time series is 'abbacab'. For further information on ABBA, see [1].

Install python packages:

pip install -r requirements.txt

If scl = 0 or scl = np.inf, then ABBA will attempt to use a C++ implementation of the CKmeans algorithm. For this we use SWIG, an open source Simplified Wrapper and Interface Generator. SWIG generated a shared library and corresponding python file which provides a wrapper for the C++ function. SWIG can be installed via the following commands:

Linux (Ubuntu):

sudo apt-get install swig

Mac:

brew install swig

Once installed, the Python wrapper can be constructed using the makefile.

make

Run the unit tests by the following command:

python test_ABBA.py -v

>>> from ABBA import ABBA
>>> ts = [-1, 0.1, 1.3, 2, 1.9, 2.4, 1.8, 0.8, -0.5]
>>> abba = ABBA()
>>> string, centers = abba.transform(ts)
Compression rate:  77.77777777777779
Digitization: Using 4 symbols
>>> reconstructed_ts = abba.inverse_transform(string, centers, ts[0])
>>> print([round(i, 1) for i in reconstructed_ts])
[-1, 0.1, 1.3, 1.9, 1.5, 2.1, 1.8, 0.6, -0.5]

• Make patches produce reconstruction that is the same length as original time series.
• Isolate clustering from digitization. This will allow us to convert time series using already constructed ABBA representation.

This project is licensed under the MIT License - see the LICENSE.md file for details

• All contributors to Ckmeans.1d.dp R package (https://cran.r-project.org/web/packages/Ckmeans.1d.dp/index.html)
• All contributors to the UCR Time Series Classification Archive (https://www.cs.ucr.edu/~eamonn/time_series_data_2018/)
• Timothy D. Butters for help with C++ and SWIG
• Massimiliano Fasi for the performance profiling code

[1] S. Elsworth and S. Güttel. ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series, MIMS Eprint 2019.11 (http://eprints.maths.manchester.ac.uk/2712/), Manchester Institute for Mathematical Sciences, The University of Manchester, UK, 2019.