Shapelet-based Temporal Association Rule Mining for Multivariate Time Series Classification

Bahri, Omar; Li, Peiyu; Boubrahimi, Soukaïna Filali; Hamdi, Shah Muhammad

doi:10.1109/bigdata55660.2022.10020478

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…The second classifier is the ST, which has been frequently used in different fields of study (Arul & Kareem 2021;Bahri et al 2022bBahri et al , 2022cLi et al 2022aLi et al , 2022b. Research on shapelets has gained considerable attention, primarily because of their highly interpretable nature (Bahri et al 2022b).…”

Section: Classification Modelsmentioning

confidence: 99%

“…Unlike non-SEP events, SEP events are extremely rare, occurring less than 100 times for some energy levels in almost 30 yr. As a consequence, there are not enough data samples to train ML models. An effective way to deal with this issue is artificially increasing the training data size (Boubrahimi et al 2016;Wen et al 2020;Li et al 2021;Alshammari et al 2022;Bahri et al 2022aBahri et al , 2023a. Time series data augmentation has been successfully applied to improve the performance of multiple predictive tasks, such as forecasting, anomaly detection, and classification.…”

Section: Introductionmentioning

confidence: 99%

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Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation

Hosseinzadeh,

Filali Boubrahimi,

Hamdi

2024

ApJS

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Solar energetic particles (SEPs) are associated with extreme solar events that can cause major damage to space- and ground-based life and infrastructure. High-intensity SEP events, particularly ∼100 MeV SEP events, can pose severe health risks for astronauts owing to radiation exposure and affect Earth’s orbiting satellites (e.g., Landsat and the International Space Station). A major challenge in the SEP event prediction task is the lack of adequate SEP data because of the rarity of these events. In this work, we aim to improve the prediction of ∼30, ∼60, and ∼100 MeV SEP events by synthetically increasing the number of SEP samples. We explore the use of a univariate and multivariate time series of proton flux data as input to machine-learning-based prediction methods, such as time series forest (TSF). Our study covers solar cycles 22, 23, and 24. Our findings show that using data augmentation methods, such as the synthetic minority oversampling technique, remarkably increases the accuracy and F1-score of the classifiers used in this research, especially for TSF, where the average accuracy increased by 20%, reaching around 90% accuracy in the ∼100 MeV SEP prediction task. We also achieved higher prediction accuracy when using the multivariate time series data of the proton flux. Finally, we build a pipeline framework for our best-performing model, TSF, and provide a comprehensive hierarchical classification of the ∼100, ∼60, and ∼30 MeV and non-SEP prediction scenarios.

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Section: Classification Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation

Hosseinzadeh,

Filali Boubrahimi,

Hamdi

2024

ApJS

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“…This algorithm represents a distinct approach predominantly used for time series data [411], [412]. This technique identifies shapelets, which are representative subsequences within a time series, facilitating the discovery of inherent patterns in the data [413].…”

Section: ) Shapelet-based Clusteringmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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“…Additionally, Stumpo et al (2021) employed SVM and LR methods to achieve SEP predictions similar to the empirical model for solar proton events real time alert concept (Laurenza et al, 2009). Furthermore, multivariate time series data augmentation techniques have been effectively applied to ML models to enhance the prediction of SEP events (Bahri et al, 2023;Hosseinzadeh et al, 2023.…”

Section: Introductionmentioning

confidence: 99%

Toward Enhanced Prediction of High‐Impact Solar Energetic Particle Events Using Multimodal Time Series Data Fusion Models

Hosseinzadeh,

Filali Boubrahimi,

Hamdi

2024

Space Weather

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Solar energetic particle (SEP) events, originating from solar flares and Coronal Mass Ejections, present significant hazards to space exploration and technology on Earth. Accurate prediction of these high‐energy events is essential for safeguarding astronauts, spacecraft, and electronic systems. In this study, we conduct an in‐depth investigation into the application of multimodal data fusion techniques for the prediction of high‐energy SEP events, particularly ∼100 MeV events. Our research utilizes six machine learning (ML) models, each finely tuned for time series analysis, including Univariate Time Series (UTS), Image‐based model (Image), Univariate Feature Concatenation (UFC), Univariate Deep Concatenation (UDC), Univariate Deep Merge (UDM), and Univariate Score Concatenation (USC). By combining time series proton flux data with solar X‐ray images, we exploit complementary insights into the underlying solar phenomena responsible for SEP events. Rigorous evaluation metrics, including accuracy, F1‐score, and other established measures, are applied, along with K‐fold cross‐validation, to ensure the robustness and generalization of our models. Additionally, we explore the influence of observation window sizes on classification accuracy.

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Shapelet-based Temporal Association Rule Mining for Multivariate Time Series Classification

Cited by 6 publications

References 41 publications

Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation

Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation

K-Means and Alternative Clustering Methods in Modern Power Systems

Toward Enhanced Prediction of High‐Impact Solar Energetic Particle Events Using Multimodal Time Series Data Fusion Models

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