Symbolization of time-series: An evaluation of SAX, Persist, and ACA

Symbolization of time-series has successfully been used to extract temporal patterns from experimental data. Segmentation is an unavoidable step of the symbolization process, and it may be characterized on two domains: the amplitude and the temporal domain. These two groups of methods present advantages and disadvantages each. Can their performance be estimated a priori based on signal characteristics? This paper evaluates the performance of SAX, Persist and ACA on 47 different time-series, based on signal periodicity. Results show that SAX tends to perform best on random signals whereas ACA may outperform the other methods on highly periodic signals. However, results do not support that a most adequate method may be determined a priory.