TrSAX—An improved time series symbolic representation for classification

Ruan, Hui; Hu, Xin; Xiao, Jin

doi:10.1016/j.isatra.2019.11.018

Cited by 14 publications

(5 citation statements)

References 17 publications

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“…For instance, Afzal et al extracted low-dimensional features using Independent Component Analysis (ICA) and utilized an enhanced K-means algorithm for clustering [ 20 ]. Zakaria et al introduced the shapelets concept, identifying key local patterns within time series [ 21 ], while Ruan et al simplified time series into symbolic sequences with Symbolic Aggregate Approximation (SAX), mitigating the stringent constraints on time series trends [ 22 ]. However, relying solely on features extracted through traditional methods for clustering does not adequately capture all the essential attributes of time series [ 19 ].…”

Section: Related Work and Basic Algorithmsmentioning

confidence: 99%

Research on load clustering algorithm based on variational autoencoder and hierarchical clustering

Cai,

Zheng,

Peng

et al. 2024

PLoS ONE

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Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE’s potential in time series clustering anticipated in future research.

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Section: Related Work and Basic Algorithmsmentioning

confidence: 99%

Research on load clustering algorithm based on variational autoencoder and hierarchical clustering

Cai,

Zheng,

Peng

et al. 2024

PLoS ONE

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“…Finally, each average value is mapped to a set of breakpoints computed based on the Gaussian distribution to assign the corresponding symbol. Despite SAX's advantages, SAX has been criticized for its Gaussian distribution assumption and its inevitable information loss when the dimensionality of the data is reduced [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-breakpoints and a Multi-objective Evolutionary Algorithm

Márquez-Grajales,

Mezura-Montes,

Acosta-Mesa

et al. 2024

Preprint

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The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which requires a computational cost for evaluating each objective function. Therefore, surrogate models were implemented to minimize this disadvantage. Nevertheless, each solution found by eMODiTS is a different-sized vector, so the surrogate model must be able to handle data sets under this characteristic. Consequently, this work's contribution lies in analyzing the surrogate models' implementation on the time series discretization, where each potential scheme is a real-number different-sized vector. For this reason, the surrogate model proposed was k-nearest Neighbors for regression with Dynamic Time Warping as a distance measure. Results suggest our proposal finds a suitable approximation to the final eMODiTS solutions with a functions evaluation reduction rate between 15% and 95%. However, according to Pareto front performance measures, the proposal Pareto front is competitive compared to the eMODiTS Pareto front, reaching an average Generational Distance (GD) between 0.0447 and 0.0536. Moreover, the average Hypervolume Ratio (HVR) ranges between 0.334 and 0.3891. Finally, our proposal compared against SAX-based methods presents a similar behavior regarding classification tasks and statistical tests.

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“…For example, two noisy time series may be of different lengths and their values may range over different scales, but still their "shapes" might be perceived as similar [5,17]. To address such problems, many high-level representations of time series have been proposed, including numerical transforms (e.g., based on the discrete Fourier transform [29,49], discrete wavelet transform [4,49], singular value decomposition [30], or piecewise linear representations [9,27,26]) and symbolic representations (such as SAX [32,33], 1d-SAX [35], many other SAX variants [45,40,6,53,51,42,31,18]).…”

Section: Introductionmentioning

confidence: 99%

An efficient aggregation method for the symbolic representation of temporal data

Chen¹,

Güttel²

2022

Preprint

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Symbolic representations are a useful tool for the dimension reduction of temporal data, allowing for the efficient storage of and information retrieval from time series. They can also enhance the training of machine learning algorithms on time series data through noise reduction and reduced sensitivity to hyperparameters. The adaptive Brownian bridge-based aggregation (ABBA) method is one such effective and robust symbolic representation, demonstrated to accurately capture important trends and shapes in time series. However, in its current form the method struggles to process very large time series. Here we present a new variant of the ABBA method, called fABBA. This variant utilizes a new aggregation approach tailored to the piecewise representation of time series. By replacing the k-means clustering used in ABBA with a sorting-based aggregation technique, and thereby avoiding repeated sum-of-squares error computations, the computational complexity is significantly reduced. In contrast to the original method, the new approach does not require the number of time series symbols to be specified in advance. Through extensive tests we demonstrate that the new method significantly outperforms ABBA with a considerable reduction in runtime while also outperforming the popular SAX and 1d-SAX representations in terms of reconstruction accuracy. We further demonstrate that fABBA can compress other data types such as images.

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TrSAX—An improved time series symbolic representation for classification

Cited by 14 publications

References 17 publications

Research on load clustering algorithm based on variational autoencoder and hierarchical clustering

Research on load clustering algorithm based on variational autoencoder and hierarchical clustering

A Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-breakpoints and a Multi-objective Evolutionary Algorithm

An efficient aggregation method for the symbolic representation of temporal data

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