  The increasing availability of temporal data poses a challenge to time-series
and signal-processing domains due to its high numerosity and complexity.
Symbolic representation outperforms raw data in a variety of engineering
applications due to its storage efficiency, reduced numerosity, and noise
reduction. The most recent symbolic aggregate approximation technique called
ABBA demonstrates outstanding performance in preserving essential shape
information of time series and enhancing the downstream applications. However,
ABBA cannot handle multiple time series with consistent symbols, i.e., the same
symbols from distinct time series are not identical. Also, working with
appropriate ABBA digitization involves the tedious task of tuning the
hyperparameters, such as the number of symbols or tolerance. Therefore, we
present a joint symbolic aggregate approximation that has symbolic consistency,
and show how the hyperparameter of digitization can itself be optimized
alongside the compression tolerance ahead of time. Besides, we propose a novel
computing paradigm that enables parallel computing of symbolic approximation.
The extensive experiments demonstrate its superb performance and outstanding
speed regarding symbolic approximation and reconstruction.
