Temporal Rule-Based Counterfactual Explanations for Multivariate Time Series

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

doi:10.1109/icmla55696.2022.00200

Cited by 9 publications

(2 citation statements)

References 23 publications

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“…They have been widely used in numerous sequence learning tasks such as machine translation (Bahdanau et al 2014) and text summarization (Cascalheira et al 2022). They process sequential data by maintaining an internal state or memory, allowing them to retain information about past inputs and make context-based decisions (Bahri et al 2022;Hosseinzadeh et al 2023). LSTM is a type of RNN architecture that captures and retains longterm dependencies in sequential data by incorporating specialized memory cells (Hochreiter & Schmidhuber 1997;Yu et al 2019).…”

Section: Related Workmentioning

confidence: 99%

Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions

Alshammari,

Hamdi,

Filali Boubrahimi

2024

ApJS

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Photospheric magnetic field parameters are frequently used to analyze and predict solar events. Observation of these parameters over time, i.e., representing solar events by multivariate time-series (MVTS) data, can determine relationships between magnetic field states in active regions and extreme solar events, e.g., solar flares. We can improve our understanding of these events by selecting the most relevant parameters that give the highest predictive performance. In this study, we propose a two-step incremental feature selection method for MVTS data using a deep-learning model based on long short-term memory (LSTM) networks. First, each MVTS feature (magnetic field parameter) is evaluated individually by a univariate sequence classifier utilizing an LSTM network. Then, the top performing features are combined to produce input for an LSTM-based multivariate sequence classifier. Finally, we tested the discrimination ability of the selected features by training downstream classifiers, e.g., Minimally Random Convolutional Kernel Transform and support vector machine. We performed our experiments using a benchmark data set for flare prediction known as Space Weather Analytics for Solar Flares. We compared our proposed method with three other baseline feature selection methods and demonstrated that our method selects more discriminatory features compared to other methods. Due to the imbalanced nature of the data, primarily caused by the rarity of minority flare classes (e.g., the X and M classes), we used the true skill statistic as the evaluation metric. Finally, we reported the set of photospheric magnetic field parameters that give the highest discrimination performance in predicting flare classes.

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Section: Related Workmentioning

confidence: 99%

Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions

Alshammari,

Hamdi,

Filali Boubrahimi

2024

ApJS

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“…Another relevant work is Temporal Rule Counterfactual Explainer (TeRCE) [30], where discriminative shapelets are extracted for each class, as well as for the NUN.…”

Section: Counterfactual Explanations For Time Series Datamentioning

confidence: 99%

Sub-SpaCE: Subsequence-based Sparse Counterfactual Explanations for Time Series Classification Problems

Refoyo,

Luengo

2023

Preprint

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The interpretation of existing machine learning models has become a critical task to facilitate the widespread adoption of AI across different domains, leading to the apparition of eXplainable AI (XAI). However, there exists an imbalance between methods tailored for specific data domains, with dedicated approaches for time series data having received limited attention until now. Moreover, current approaches often overlook the unique challenges present in time series data. In this paper, we introduce Subsequence-based Sparse Counterfactual Explanations (Sub-SpaCE), a novel method tailored for time series classification problems. Sub-SpaCE employs genetic algorithms with customized mutation and initialization processes, promoting changes in a small number of subsequences to generate highly sparse and plausible counterfactual explanations. Our empirical evaluations on various datasets demonstrate Sub-SpaCE's superior performance, achieving an optimal balance between sparsity and plausibility in counterfactual explanations for time series data.

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