Motif Alignment for Time Series Data Augmentation

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

doi:10.1007/978-3-031-39831-5_4

Cited by 2 publications

(1 citation statement)

References 7 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). ST is a technique used in time series classification that extracts discriminative and important features.…”

Section: Classification Modelsmentioning

confidence: 99%

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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