MrSQM: Fast Time Series Classification with Symbolic Representations

Abstract

Symbolic representations of time series have proven to be effective for time series classification, with many recent approaches including SAX-VSM, BOSS, WEASEL, and MrSEQL. The key idea is to transform numerical time series to symbolic representations in the time or frequency domain, i.e., sequences of symbols, and then extract features from these sequences. While achieving high accuracy, existing symbolic classifiers are computationally expensive. In this paper we present MrSQM, a new time series classifier which uses multiple symbolic representations and efficient sequence mining, to extract important time series features. We study four feature selection approaches on symbolic sequences, ranging from fully supervised, to unsupervised and hybrids. We propose a new approach for optimal supervised symbolic feature selection in all-subsequence space, by adapting a Chi-squared bound developed for discriminative pattern mining, to time series. Our extensive experiments on 112 datasets of the UEA/UCR benchmark demonstrate that MrSQM can quickly extract useful features and learn accurate classifiers with the classic logistic regression algorithm. Interestingly, we find that a very simple and fast feature selection strategy can be highly effective as compared with more sophisticated and expensive methods. MrSQM advances the state-of-the-art for symbolic time series classifiers and it is an effective method to achieve high accuracy, with fast runtime.

HYDRA: Competing convolutional kernels for fast and accurate time series classification

“…BOSS was identified in Bagnall et al (2017) (the 'bake off' paper) as one of the three most accurate methods for time series classification on the datasets in the UCR archive (Dau et al, 2019), and was included in the original HIVE-COTE ensemble, then the most accurate method for time series classification on the datasets in the UCR archive (Lines et al, 2018). The most accurate current dictionary methods, TDE (Middlehurst et al, 2020a) and MrSQM (Le Nguyen and Ifrim, 2022), are competitive with several of the Hydra most accurate current methods for time series classification on the datasets in the UCR archive. TDE is one of the four components of HIVE-COTE 2 (HC2), currently the most accurate method for time series classification on the datasets in the UCR archive (Middlehurst et al, 2021).…”

“…The resulting feature space is typically very large and very sparse (see Schäfer and Leser, 2017;Large et al, 2019;Le Nguyen and Ifrim, 2022), and the resulting patterns represent a high degree of approximation, as the input is both smoothed and quantised to a very small set of discrete values. In addition, for methods using SFA or a variation thereof, the patterns are formed over values in the frequency domain, rather than the original input.…”

“…Despite the broad similarities between many dictionary methods, different methods can produce very different results due to differences in the way that the input is approximated or quantised (Bagnall et al, 2017;Large et al, 2019). The most prominent dictionary methods are BOSS (Schäfer, 2015), cBOSS (Middlehurst et al, 2019), S-BOSS (Large et al, 2019), WEASEL (Schäfer and Leser, 2017) and, more recently, TDE (Middlehurst et al, 2020a) and MrSQM (Le Nguyen and Ifrim, 2022).…”

“…This ranking is performed on only 106 of the 112 datasets, as results for S-BOSS and WEASEL are unavailable for the six largest datasets due to computational constraints (see Middlehurst et al, 2020a). We have obtained the results for MrSQM using the implementation available at https://github.com/mlgig/mrsqm, with the hyperparameter configuration as set out in Le Nguyen and Ifrim (2022).…”