A multi-breakpoints approach for symbolic discretization of time series

Márquez-Grajales, Aldo; Acosta-Mesa, Héctor-Gabriel; Mezura-Montes, Efrén; Graff, Mario

doi:10.1007/s10115-020-01437-4

Cited by 12 publications

(4 citation statements)

References 38 publications

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“…eMODiTS is a SAX-based discretization approach [20]. Thus, it employs the PAA technique to reduce dimensionality, word segment cuts, alphabet cuts, and the same symbol assignment process.…”

Section: The Enhanced Multi-objective Symbolic Discretization For Tim...mentioning

confidence: 99%

“…Márquez et al [33] is one of the few works in which surrogate models were implemented to minimize the computational cost by identifying an appropriate discretization scheme for temporal data. The researchers applied surrogate models to the enhanced multi-objective symbolic discretization for time series (eMODiTS) [20]. eMODiTS is a temporal data mining technique that discretizes time series by a unique set of value cuts for each time cut and three objective functions.…”

Section: Introductionmentioning

confidence: 99%

“…Parameter setting for eMODiTS and sMODiTS. The values were selected according to the reported in[20] …”

mentioning

confidence: 99%

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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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“…eMODiTS is a SAX-based discretization approach [20]. Thus, it employs the PAA technique to reduce dimensionality, word segment cuts, alphabet cuts, and the same symbol assignment process.…”

Section: The Enhanced Multi-objective Symbolic Discretization For Tim...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…In Sokolovska et al (2018), a provable algorithm is considered for learning scoring systems with continuous feature binning. In order to reduce time series dimensionality and cardinality, a multi-breakpoints approach is employed to discretize continuous data (Márquez-Grajales et al, 2020) or some statistical test is employed (Abachi et al, 2018). Also discrete features are closer to a knowledge-level representation that is easy to understand, use and explain than continuous ones (Tsai and Chen, 2019).…”

Section: Introductionmentioning

confidence: 99%

Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold Regression

Lin¹,

Zhou²,

Geng³

et al. 2022

Preprint

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In traditional logistic regression models, the link function is often assumed to be linear and continuous in predictors. Here, we consider a threshold model that all continuous features are discretized into ordinal levels, which further determine the binary responses. Both the threshold points and regression coefficients are unknown and to be estimated. For high dimensional data, we propose a FusIon penalized Logistic ThrEshold Regression(FILTER) model, where a fused lasso penalty is employed to control the total variation and shrink the coefficients to zero as a method of variable selection. Under mild conditions on the estimate of unknown threshold points, we establish the non-asymptotic error bound for coefficient estimation and the model selection consistency. With a careful characterization of the error propagation, we have also shown that the tree-based method, such as CART, fulfil the threshold estimation conditions. We find the FILTER model is well suited in the problem of early detection and prediction for chronic disease like diabetes, using physical examination data. The finite sample behaviour of our proposed method are also explored and compared with extensive Monte Carlo studies, which supports our theoretical discoveries.

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Overview of Time Series Classification Based on Symbolic Discretization for ECG Applications

Taktak,

Triki

2023

Communications in Computer and Information Science

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A multi-breakpoints approach for symbolic discretization of time series

Cited by 12 publications

References 38 publications

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

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

Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold Regression

Overview of Time Series Classification Based on Symbolic Discretization for ECG Applications

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