Abstract Machine learning models often excel in prediction tasks but frequently lack interpretability, limiting their use in critical domains where understanding and trust are essential. Counterfactual explanations bridge this gap by identifying the minimal feature modifications necessary to alter a model’s prediction to a desired outcome. This paper offers a comprehensive review and evaluation of counterfactual explanation methods specifically for univariate time series classification. By categorizing methods based on their underlying principles, we highlight their strengths, limitations, and the inherent compromises between properties such as validity, proximity, sparsity, and plausibility. Our experimental evaluation assesses these key properties across diverse datasets. The results make it evident that no current method achieves all these properties simultaneously. By synthesizing advancements and pinpointing areas for improvement, this work aims to guide future research and foster the development of interpretable AI solutions for time series applications.

Backdoor attacks are a significant threat to the performance and integrity of pre-trained language models. Although such models are routinely fine-tuned for downstream NLP tasks, recent work shows they remain vulnerable to backdoor attacks that survive vanilla fine-tuning. These attacks are difficult to defend because end users typically lack knowledge of the attack triggers. Such attacks consist of stealthy malicious triggers introduced through subtle syntactic or stylistic manipulations, which can bypass traditional detection and remain in the model, making post-hoc purification essential. In this study, we explore whether attention-head pruning can mitigate these threats without any knowledge of the trigger or access to a clean reference model. To this end, we design and implement six pruning-based strategies: (i) gradient-based pruning, (ii) layer-wise variance pruning, (iii) gradient-based pruning with structured L1/L2 sparsification, (iv) randomized ensemble pruning, (v) reinforcement-learning-guided pruning, and (vi) Bayesian uncertainty pruning. Each method iteratively removes the least informative heads while monitoring validation accuracy to avoid over-pruning. Experimental evaluation shows that gradient-based pruning performs best while defending the syntactic triggers, whereas reinforcement learning and Bayesian pruning better withstand stylistic attacks.

Counterfactual explanations are essential for inter-preting predictions made by time series classifiers, yet existing methods often disrupt inherent temporal dependencies, resulting in unrealistic explanations. We propose CACTUS (Cross-Aligned Counterfactuals for Time Series Classification), a novel model-agnostic framework designed to generate interpretable explanations for black-box models. CACTUS leverages Dynamic Time Warping (DTW) Barycenter Averaging and introduces a cross-correlation loss to enforce temporal consistency and alignment. We introduce two variants of CACTUS: one that emphasizes minimal perturbations while maintaining classification validity, and another that ensures gradual, realistic modifications in the time series. Comprehensive experiments on diverse benchmark datasets demonstrate that CACTUS outperforms state-of-the-art optimization-based methods by generating more reliable and interpretable counterfactual explanations, establishing it as a robust tool for post-hoc time series models analysis and explanations.

Understanding the predictions of time series classifiers is essential in high-stakes domains such as health-care, finance, and security. Counterfactual explanations, which identify minimal perturbations to input sequences that alter model predictions, provide intuitive and actionable insights. However, existing methods often struggle to generate realistic or diverse counterfactuals, especially for sequential data with strong temporal dependencies. In this work, we propose DiverseLCF, a post-hoc framework for generating diverse and plausible counterfactual explanations for time series classifiers. By optimizing in the latent space of a pretrained variational autoencoder (VAE), DiverseLCF ensures that generated counterfactuals remain temporally coherent and aligned with the data manifold. We formulate the generation process as a multi-objective optimization problem that balances validity, proximity, and diversity. Extensive experiments show that DiverseLCF consistently generates valid, plausible, and diverse coun-terfactuals with minimal proximity trade-offs, providing a robust and generalizable tool for interpreting time series models.

Generating high-quality synthetic time series is a fundamental yet challenging task across domains such as forecasting and anomaly detection, where real data can be scarce, noisy, or costly to collect. Unlike static data generation, synthesizing time series requires modeling both the marginal distribution of observations and the conditional temporal dependencies that govern sequential dynamics. We propose TIMED, a unified generative framework that integrates a denoising diffusion probabilistic model (DDPM) to capture global structure via a forward-reverse diffusion process, a supervisor network trained with teacher forcing to learn autoregressive dependencies through next-step prediction, and a Wasserstein critic that provides adversarial feedback to ensure temporal smoothness and fidelity. To further align the real and synthetic distributions in feature space, TIMED incorporates a Maximum Mean Discrepancy (MMD) loss, promoting both diversity and sample quality. All components are built using masked attention architectures optimized for sequence modeling and are trained jointly to effectively capture both unconditional and conditional aspects of time series data. Experimental results across diverse multivariate time series benchmarks demonstrate that TIMED generates more realistic and temporally coherent sequences than state-of-the-art generative models.

Data augmentation can significantly enhance the performance of machine learning tasks by addressing data scarcity and improving generalization. However, generating time series data presents unique challenges. A model must not only learn a probability distribution that reflects the real data distribution but also capture the conditional distribution at each time step to preserve the inherent temporal dependencies. To address these challenges, we introduce AVATAR, a framework that combines Adversarial Autoencoders (AAE) with Autoregressive Learning to achieve both objectives. Specifically, our technique integrates the autoencoder with a supervisor and introduces a novel supervised loss to assist the decoder in learning the temporal dynamics of time series data. Additionally, we propose another innovative loss function, termed distribution loss, to guide the encoder in more efficiently aligning the aggregated posterior of the autoencoder’s latent representation with a prior Gaussian distribution. Furthermore, our framework employs a joint training mechanism to simultaneously train all networks using a combined loss, thereby fulfilling the dual objectives of time series generation. We evaluate our technique across a variety of time series datasets with diverse characteristics. Our experiments demonstrate significant improvements in both the quality and practical utility of the generated data, as assessed by various qualitative and quantitative metrics.

Current Generative Adversarial Network (GAN)-based approaches for time series generation face challenges such as suboptimal convergence, information loss in embedding spaces, and instability. To overcome these challenges, we introduce an advanced framework that integrates the advantages of an autoencoder-generated embedding space with the adversarial training dynamics of GANs. This method employs two discriminators: one to specifically guide the generator and another to refine both the autoencoder’s and generator’s output. Additionally, our framework incorporates a novel autoencoder-based loss function and supervision from a teacher-forcing supervisor network, which captures the stepwise conditional distributions of the data. The generator operates within the latent space, while the two discriminators work on latent and feature spaces separately, providing crucial feedback to both the generator and the autoencoder. By leveraging this dual-discriminator approach, we minimize information loss in the embedding space. Through joint training, our framework excels at generating high-fidelity time series data, consistently outperforming existing state-of-the-art benchmarks both qualitatively and quantitatively across a range of real and synthetic multivariate time series datasets.

EXplainable Artificial Intelligence (XAI) methods are essential for understanding complex machine learning models. This paper presents Adaptive Counterfactual Explanations for Time Series (ACTS), a model-agnostic algorithm that improves the interpretability, sparsity, and contiguity of counterfactuals. ACTS leverages Barycenter Averaging (DBA) with Dynamic Time Warping (DTW) and a regularized loss function combining prediction, DBA, L1 regularization, sparsity, and smoothness terms. Empirical results on real-world datasets show ACTS generates high-quality explanations with fewer outliers and better interpretability than current methods, advancing XAI in time series analysis.

Artificial intelligence (AI) and algorithmic decision-making are profoundly shaping various aspects of society, with applications in healthcare, business, education, etc. As these systems become more integral to high-stakes decisions, concerns about their transparency and interpretability are growing. To address these concerns, explainable AI (XAI) methods have been developed, with counterfactual explanations emerging as a powerful tool. Counterfactuals help users understand AI decisions by demonstrating how small changes in input could alter the outcome, providing a clear and intuitive way to interpret AI behavior. Despite their potential, generating valid, interpretable, and efficient counterfactual explanations is particularly challenging in time series domains, where data points are interdependent. In this paper, we introduce a novel approach to counterfactual explanations guided by ShapeDTW Barycenter Averaging (ShapeDBA). By integrating ShapeDBA into the counterfactual generation process, we ensure that the produced explanations are not only valid and interpretable but also efficient to generate. Our approach provides counterfactuals that align closely with human intuition while maintaining the computational efficiency required for practical deployment. This work represents a significant step forward in the development of interpretable AI systems, particularly in the complex domain of time series analysis.

In heliophysics research, predicting solar flares is crucial due to their potential to substantially impact both space-based systems and Earth’s infrastructure. Magnetic field data from solar active regions, recorded by solar imaging observatories, are transformed into multivariate time series to enable solar flare prediction using temporal window-based analysis. In the realm of multivariate time series-driven solar flare prediction, addressing severe class imbalance with effective strategies for multivariate time series representation learning is key to developing robust predictive models. Traditional methods often struggle with overfitting to the majority class in prediction tasks where major solar flares are infrequent. This work presents EX-CON, a contrastive representation learning framework designed to enhance classification performance amidst such imbalances. EXCON operates through four stages: (1) obtaining core features from multivariate time series data; (2) selecting distinctive contrastive representations for each class to maximize inter-class separation; (3) training a temporal feature embedding module with a custom extreme reconstruction loss to minimize intra-class variation; and (4) applying a classifier to the learned embeddings for robust classification. The proposed method leverages contrastive learning principles to map similar instances closer in the feature space while distancing dissimilar ones, a strategy not extensively explored in solar flare prediction tasks. This approach not only addresses class imbalance but also offers a versatile solution applicable to both univariate and multivariate time series across binary and multiclass classification problems. Experimental results, including evaluations on the benchmark solar flare dataset and multiple time series archive datasets with binary and multiclass labels, demonstrate EXCON’s efficacy in enhancing classification performance and reducing overfitting.

Accurate solar flare prediction is crucial due to the significant risks that intense solar flares pose to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by utilizing advanced data preprocessing and classification methods on a multivariate time series-based dataset of photospheric magnetic field parameters. First, our study employs a novel preprocessing pipeline that includes missing value imputation, normalization, balanced sampling, near decision boundary sample removal, and feature selection to significantly boost prediction accuracy. Second, we integrate contrastive learning with a GRU regression model to develop a novel classifier, termed ContReg, which employs dual learning methodologies, thereby further enhancing prediction performance. To validate the effectiveness of our preprocessing pipeline, we compare and demonstrate the performance gain of each step, and to demonstrate the efficacy of the ContReg classifier, we compare its performance to that of sequence-based deep learning architectures, machine learning models, and findings from previous studies. Our results illustrate exceptional True Skill Statistic (TSS) scores, surpassing previous methods and highlighting the critical role of precise data preprocessing and classifier development in time series-based solar flare prediction.

Generating time series data using Generative Adversarial Networks (GANs) presents several prevalent challenges, such as slow convergence, information loss in embedding spaces, instability, and performance variability depending on the se-ries length. To tackle these obstacles, we introduce a robust framework aimed at addressing and mitigating these issues effectively. This advanced framework integrates the benefits of an Autoencoder-generated embedding space with the adversarial training dynamics of GANs. This framework benefits from a time series-based loss function and oversight from a supervisory net-work, both of which capture the stepwise conditional distributions of the data effectively. The generator functions within the latent space, while the discriminator offers essential feedback based on the feature space. Moreover, we introduce an early generation algorithm and an improved neural network architecture to enhance stability and ensure effective generalization across both short and long time series. Through joint training, our framework consistently outperforms existing benchmarks, generating high-quality time series data across a range of real and synthetic datasets with diverse characteristics.

Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive success in a wide range of applications and tasks. The challenge of many state-of-the-art ML models is a lack of transparency and interpretability. In this work, we introduce M-CELS, a counterfactual explanation model designed to enhance interpretability in multidimensional time series classification tasks. Our experimental validation involves comparing M-CELS with leading state-of-the-art baselines, utilizing seven real-world time-series datasets from the UEA repository. The results demonstrate the superior performance of M-CELS in terms of validity, proximity, and sparsity, reinforcing its effectiveness in providing transparent insights into the decisions of machine learning models applied to multivariate time series data.

Major solar flares are abrupt surges in the Sun's magnetic flux, presenting significant risks to technological infrastructure. In view of this, effectively predicting major flares from solar active region magnetic field data through machine learning methods becomes highly important in space weather research. Magnetic field data can be represented in multivariate time series modality where the data displays an extreme class imbalance due to the rarity of major flare events. In time series classification-based flare prediction, the use of contrastive representation learning methods has been relatively limited. In this paper, we introduce CONTREX, a novel contrastive representation learning approach for multivariate time series data, addressing challenges of temporal dependencies and extreme class imbalance. Our method involves extracting dynamic features from the multivariate time series instances, deriving two extremes from positive and negative class feature vectors that provide maximum separation capability, and training a sequence representation embedding module with the original multivariate time series data guided by our novel contrastive reconstruction loss to generate embeddings aligned with the extreme points. These embeddings capture essential time series characteristics and enhance discriminative power. Our approach shows promising solar flare prediction results on the Space Weather Analytics for Solar Flares (SWAN-SF) multivariate time series benchmark dataset against baseline methods.

Minority stress is the leading theoretical construct for understanding LGBTQ+ health disparities. As such, there is an urgent need to develop innovative policies and technologies to reduce minority stress. To spur technological innovation, we created the largest labeled datasets on minority stress using natural language from subreddits related to sexual and gender minority people. A team of mental health clinicians, LGBTQ+ health experts, and computer scientists developed two datasets: (1) the publicly available LGBTQ+ Minority Stress on Social Media (MiSSoM) dataset and (2) the advanced request-only version of the dataset, LGBTQ+ MiSSoM+. Both datasets have seven labels related to minority stress, including an overall composite label and six sublabels. LGBTQ+ MiSSoM (N = 27,709) includes both human- and machine-annotated la-bels and comes preprocessed with features (e.g., topic models, psycholinguistic attributes, sentiment, clinical keywords, word embeddings, n-grams, lexicons). LGBTQ+ MiSSoM+ includes all the characteristics of the open-access dataset, but also includes the original Reddit text and sentence-level labeling for a subset of posts (N = 5,772). Benchmark supervised machine learning analyses revealed that features of the LGBTQ+ MiSSoM datasets can predict overall minority stress quite well (F1 = 0.869). Benchmark performance metrics yielded in the prediction of the other labels, namely prejudiced events (F1 = 0.942), expected rejection (F1 = 0.964), internalized stigma (F1 = 0.952), identity concealment (F1 = 0.971), gender dysphoria (F1 = 0.947), and minority coping (F1 = 0.917), were excellent. Descriptive analyses, ethical considerations, limitations, and possible use cases are provided.

Abstract The opacity inherent in machine learning models presents a significant hindrance to their widespread incorporation into decision-making processes. To address this challenge and foster trust among stakeholders while ensuring decision fairness, the data mining community has been actively advancing the explainable artificial intelligence paradigm. This paper contributes to the evolving field by focusing on counterfactual generation for time series classification models, a domain where research is relatively scarce. We develop, a post-hoc, model agnostic counterfactual explanation algorithm that leverages the Matrix Profile to map time series discords to their nearest neighbors in a target sequence and use this mapping to generate new counterfactual instances. To our knowledge, this is the first effort towards the use of time series discords for counterfactual explanations. We evaluate our algorithm on the University of California Riverside and University of East Anglia archives and compare it to three state-of-the-art univariate and multivariate methods.

Data augmentation plays an important part in the current success of machine learning and deep learning models. In particular, state-of-the-art architectures in the image recognition field include data augmentation modules as an integral part. However, there is still room for progress in the time series domain. In this work, we introduce OptimAug, a novel method for time series data augmentation. We deviate from the current state-of-the-art comprised of random transformations, pattern mixing, generative models, and decomposition methods, to develop the first multiloss-based optimization method. We evaluate our method with its two variants on datasets from the University of California Riverside (UCR) archive and compare it to multiple baseline algorithms from the literature.

LGBTQ+ minority stress is a pervasive form of anti-LGBTQ+ adverse events and psychological strain that drives health inequities among LGBTQ+ people. Minority stress is also linguistically sophisticated (e.g., composed of cultural idioms, psycholinguistic permutations, and lexical density). Because minority stress is a linguistically sophisticated social determinant of health disparities, it is challenging to detect using natural language processing (NLP). Using 5,789 human-annotated Reddit posts from the LGBTQ+ Minority Stress on Social Media (MiSSoM+) Dataset, we investigated and compared the performance of four neural networks and two traditional machine learning architectures in modeling minority stress at both the factor (i.e., separate components of minority stress) and composite level. A novel hybrid model combining Bidirectional Encoder Representations from Transformers and convolutional neural network (BERT-CNN) improved the prediction of composite minority stress (F1 = 0.84). Our experiments on separate factors of minority stress are the first to demonstrate that hybrid neural network models can detect semantically complex expressions of prejudiced events (F1 = 0.87), expected rejection (F1 = 0.92), internalized stigma (F1 = 0.91), identity concealment (F1 = 0.92), and minority coping (F1 = 0.84). We also substantially improved the prediction of gender dysphoria (F1 = 0.94)—a conceptually new candidate component of minority stress. Big data analytics may not be a panacea for the problem of minority stress, but our work joins a growing literature base to show that deep learning models are remarkable in detecting linguistically sophisticated social determinants of health disparities in big data, thus providing evidence in support of the potential benefit from the innovative use of such technology in eliminating group-specific health inequities.

As the demand for interpretable machine learning approaches increases, there is an increasing need for human involvement to provide diverse explanations for model decisions. This is crucial for enhancing trust and transparency in AI-based systems, leading to the emergence of the Explainable Artificial Intelligence (XAI) field. In this paper, we design a novel counterfactual explanation model, CELS, which learns a saliency map for the interest of an instance and generates a counterfactual explanation guided by the learned saliency map. CELS adopts a gradient-based approach composed of three interdependent modules that combine to generate sparse counterfactual explanations that are easily understood by end users. To the best of our knowledge, this is the first attempt to guide the perturbation to generate a counterfactual explanation via a learned saliency map. To validate our model, we conducted experiments using five popular real-world time-series datasets obtained from the UCR repository. The experimental results demonstrate the superiority of our model in achieving higher sparsity, proximity, and interpretability of counterfactual explanations when compared to other state-of-the-art baselines.

Classifying solar flares is crucial for comprehending their potential impact on space weather forecasting. In this study, we propose a novel approach to classify multivariate time series (MVTS) solar flare data using an Attention/Transformer-based framework. By utilizing the power of attention mechanisms and transformer architectures, we can capture complex temporal dependencies and interactions among different features in the time series data. Our model simultaneously attends to rele-vant features and learns their dependencies, enabling accurate classification of solar flare events. We evaluate our approach on a solar flare MVTS dataset and compare its performance against several state-of-the-art methods. The experimental results demonstrate that our approach achieves superior classification accuracy, outperforming existing ones. These findings highlight the effectiveness of attention mechanisms and transformer models in capturing the complex patterns in multivariate time series solar flare data. This research contributes to the advancement of solar physics and space weather forecasting, facilitating a deeper understanding of solar flare dynamics and enabling more accurate predictions to improve space weather forecasting capabilities.

Data augmentation has proved to be extremely beneficial to the advancement of current state-of-the-art machine learning classification models. Not only does it allow balancing datasets collected in domains with rare positive events, but it also helps increase the size of small datasets to feed data-hungry deep learning models. More often than not, this results in a significant performance increase. However, data augmentation in the time series domain is still lagging compared to other domains such as image and text data. We propose BootShape, a novel data augmentation method for time series classification that bootstraps the residual components of original dataset instances to generate new time series patterns. By preserving the most important shapelets, BootShape is able to generate realistic data instances that respect the original class distributions. Using real-life datasets from the University of California Riverside (UCR) archive, we show that BootShape leads to higher performance gains compared to 6 state-of-the-art pattern mixing time series data augmentation methods.

Multimodal predictive models have gained significant attention in recent years due to their ability to leverage data from multiple modalities, enabling a richer representation of the underlying phenomena. Traditional fusion methods face challenges in dealing with the curse of dimensionality and increased computational complexity. In this paper, we introduce a novel meta-learning model called Classification with Meta-Learning and Multimodal Stratified Time Series Forest (MET-FORC) to enhance multimodal fusion in classification tasks. METFORC utilizes a decision tree to learn from the scores generated by Time Series Forest (TSF) classifiers, enabling the effective aggregation of multimodal information. By addressing the limitations of traditional fusion techniques, METFORC offers improved classification accuracy. We validate the performance of our proposed model on three benchmark datasets from the solar weather prediction domain, showcasing its superiority over existing fusion approaches. Our findings demonstrate the effectiveness of METFORC in handling high-dimensional multimodal data and its potential for enhancing decision-making in complex tasks.

In recent years, there has been a proliferation of Time Series Classification (TSC) models that have been proposed and implemented in various real-life applications. The increase in time series availability in some scientific domains has primarily contributed to the enrichment of TSC algorithms. However, the high performance of TSC models heavily relies on a sufficiently labeled training dataset. In reality, data availability in some domains remains limited, negatively affecting the performance of TSC models. To address this challenge, we propose AGSM and AIM, aiming to enlarge the size of the training data to improve the performance of TSC. In the computer vision domain, the use of adversarial attack mechanisms to augment medical images for segmentation is very useful in improving segmentation accuracy. Following this line of work, in this paper, we propose leveraging adversarial attack mechanisms to add positive noise to generate new synthetic samples for the time series classification task. Our experimental results on 25 real-world time series data show positive impacts on improving classification performance for most datasets. Our proposed method also shows competitive performances when compared with the other seven state-of-the-art baselines.

Solar physicists frequently use solar magnetic field parameters for analyzing and predicting solar events. Temporal observation of magnetic field parameters, i.e., multivariate time series (MVTS) representation facilitates finding relationships of magnetic field states to the occurrence of extreme solar events (e.g., solar flares). Feature selection of MVTS-represented solar magnetic field parameters (features) can select the most relevant parameters that give high prediction accuracy. In this paper, we propose a deep learning-based feature selection method, more specifically, an LSTM-based incremental feature selection method, as an end-to-end solution for feature selection in MVTS data. We performed LSTM-based feature selection for multivariate time series data in two steps. Firstly, each MVTS feature is evaluated individually by an LSTM-based univariate sequence classifier, and secondly, the top-performing features are combined to produce input for a downstream LSTM-based multivariate sequence classifier. We compared the proposed MVTS feature selection method with three other baseline feature selection methods on an MVTS-based solar flare prediction dataset and demonstrated that our method selects more discriminatory features compared to other methods.

The rapid upsurge of numerical sources of information and the growth of storage capacities in recent years has resulted in the collection of massive time series datasets. Inspired by association rule mining and other rule discovery algorithms, several approaches have been proposed in the literature to discover temporal association rules from time series data. These methods place interpretability at the top of their priorities and aim to provide domain experts with relevant and qualitative rules. In this paper, we aim to fill the gap between temporal association rule mining and time series classification t asks to increase the interpretability of current classification methods. We propose rule transform (RT), a novel algorithm for multivariate time series classification ( MTSC) t hat generates discriminative temporal rules for the sake of classification. RT generates a new feature space that represents the support of the mined temporal rules which can easily be qualitatively interpreted by domain experts. The algorithm uses Allen’s Interval Algebra to extract the most prominent temporal rules from a given dataset. To our knowledge, this is the first effort to use shapelets as a unit for temporal rule mining studies for the purpose of classification. We evaluate our algorithm on the UEA archive of multivariate time series. Results show that RT produces accuracies superior to state-of-the-art time series classification algorithms with the additional advantage of interpretability.

EXplainable Artificial Intelligence ( XAI) methods have gained much momentum lately given their ability to shed the light on the decision function of opaque machine learning models. There are two dominating XAI paradigms: feature attribution and counterfactual explanation methods. While the first family of methods explains why the model made a decision, counterfactual methods aim at answering what-if the input is slightly different and results in another classification decision. Most of the research efforts have focused on answering the why question for time series data modality. In this paper, we aim at answering the what-if question by finding a good balance between a set of desirable counterfactual explanation properties. We propose Shapelet-guided Counterfactual Explanation (SG-CF), a novel optimization-based model that generates interpretable, intuitive post-hoc counterfactual explanations of time series classification models that balance validity, proximity, sparsity, and contiguity. Our experimental results on nine real-world time-series datasets show that our proposed method can generate counterfactual explanations that balance all the desirable counterfactual properties in comparison with other competing baselines.

EXplainable Artificial Intelligence (XAI) methods are increasingly accepted as effective tools to trace complex machine learning models' decision-making processes. There are two underlying XAI paradigms: (1) traditional factual methods and (2) emerging counterfactual models. The first family of methods uses feature attribution techniques that alter the feature space and observe the impact on the decision function. Counterfactual models aim at providing the smallest possible change to the feature vector that can change the prediction outcome. In this paper, we propose TimeX, a new model-agnostic time series counterfactual explanation algorithm that provides sparse, interpretable, and contiguous explanations. We validate our model using real-world time series datasets and show that our approach can generate explanations with up to 20% fewer outliers in comparison with other state-of-the-art competing baselines.

The black-box nature of machine learning models is the main reason impeding their full adoption in decision-making processes. In order to reduce models’ opacity and overpass this challenge, major efforts that aim to increase stakeholders’ trust and ensure the fairness of decisions are being made by the data mining community under the Explainable Artificial Intelligence (XAI) paradigm. The two main categories of solutions are 1) developing fully transparent algorithms and 2) providing post hoc explanations. However, the literature is rather scarce when it comes to time series data, and even more so in the context of multivariate time series. In this work, we aim to exploit the discriminative power of shapelets and temporal rules in time series mining and capitalize on their inherent interpretability to develop a model-agnostic, temporal rule counterfactual explainer (TeRCE) for multivariate time series datasets. Counterfactual explanations indicate how should the input change such that the decision output changes too. Thus, they can highly increase the interpretability of black-box models. We test TeRCE on five benchmark datasets from the UEA archive and prove that it produces high-quality counterfactuals. Moreover, we show that in addition to being visually and conceptually interpretable, our approach performs better than the state-of-the-art algorithms in terms of proximity, sparsity, and second in terms of plausibility.

Solar flare prediction has become essential in space weather research due to its potential adverse space-weather ramifications. Over recent years, a set of machine learning models on solar flare prediction have been proposed and significant improvement has been made over the previous state of the art. However, most existing research work focuses on the prediction task and ignores the interpretability behind the prediction task. In this paper, we provide a post-hoc explanation method based on solar flare prediction, FAST-CF. In particular, we incorporate the nearest unlike neighbor for guiding the counterfactual search, which is fast to search for the optimal result. In addition, FAST-CF encapsulates the desirable properties of a counterfactual explanation for solar flare prediction. We use different evaluation metrics to compare the performance of FAST-CF with the other two baselines and verify the superiority of our method to existing state-of-the-art.

Clustering is an important data mining task that consists of grouping data without a prior knowledge of classes. In the recent years, with the rise of big data and its various applications, unsupervised learning such as clustering algorithms have attracted massive interest in the data mining field. For the case of time series data, temporal changes are difficult to detect, which makes the clustering for temporal sequence data more complex than traditional vector data. In this paper, we propose a new model that generates graphs from time series data to preserve important relations between different data points. In particular, every time series data will be considered as a node, then edges will be added between nodes if the dynamic time warping distances of time series data achieve a specific threshold, then spectral clustering algorithm is applied to the generated graph. Our results shows that our new proposal time series graph representation outperforms state-of-the-art clustering algorithms.

The recent eminent success of deep learning models has triggered a lot of interest in data augmentation studies. The main challenge of data-hungry models is that they are bound to overfitting and require massive amounts of data to be trained on to achieve optimal levels of convergence. It is of high importance for any data oversampling algorithm to produce synthetic data samples that are not only stemming from the same real data distribution, but also have enough variation to provide more learning opportunities to trained models. Early efforts on time series data augmentation either rely on generating new samples by interpolating between two close real data neighbors or using Generative Adversarial Networks. In this paper, we present Time Series Generation based on Auto-encoders (TiGA), a novel algorithm for time series generation using time-warped autoencoders. Our idea is to exploit the lossy transformation of autoencoders for the purpose of generating synthetic time series data samples. To the best of our knowledge, this is the first effort that leverages the latent features generated by autoencoders for the purpose of time series data oversampling. We evaluate our proposed approach on an open-source real-life solar flare prediction dataset. Results show that TiGA produces samples that are both quantitatively and qualitatively superior to current state-of-the-art methods.

Data augmentation is an important data mining task that has been highly adopted to resolve class imbalance problems and provide more input to data-hungry models. For the case of time series data, the data augmentation method needs to take into consideration the dependence of the variables. In this paper, we propose a new model that preserves important relations between variables while performing time series data augmentation. In particular, we combine shapelets transform and Synthetic Minority Oversampling Technique (SMOTE) models to achieve the aforementioned goal. By using shapelets transform, the most prominent shapelets are extracted from the training set and used during the oversampling process. To make the most use of important shapelets, our proposed method preserves the extracted shapelets as the key part in the synthetic data sample. Then for the other parts of each synthetic data sample, we use SMOTE to generate the remaining data points. Compared with pure SMOTE, our method makes full use of important shapelets to maintain the important correlations between interdependent variables, which also can provide more interpretive information.

Machine learning-based prediction of solar flares has become an important application of data science in space weather research. Spatiotemporal magnetic field data of solar active regions captured by solar imaging observatories are mapped into multivariate time series data to facilitate temporal window-based solar flare prediction. Existing methods of solar flare prediction leveraging multivariate time series data rely heavily on statistical features for the representation learning of individual time series instances. In this work, we used Deep Learning, more specifically Long Short Term Memory (LSTM) Networks for learning representations of multivariate time series instances that map into multiple flare classes. This work enables the end-to-end multivariate time series classification bypassing the requirements of hand-engineered features. Our experiments on a real-life solar flare dataset show better prediction performance in comparison with state-of-the-art baseline methods.

Shapelets, also known as motifs, are time series sequences that have the property of discriminating between time series classes. Lately, shapelets studies have gained a lot of momentum due to their interpretable nature. As opposed to traditional time series classifiers, shapelet-based learners provide a visual representation of the pattern that triggers the classification decision. One of the most challenging issues of shapelet-based classifiers is the generation of a large number of shapelet outputs. To the best of our knowledge, this is the first effort that addresses the high numerosity problem of mined shapelets issue by mining the minimal set of discriminative shapelets for time series data. We propose a new shapelet mining learner, 1DCNN, that has the property of learning shapelets of different lengths using a black-box neural network model. 1DCNN optimizes the entire classification schema by learning the shapes of the representative patterns. Our proposed model uses network pruning to sparsify the network and keep only the most discriminative shapelets without compromising the classification accuracy. We validated our model using 59 real-world time series datasets from the UCR repository. Our experimental results show the effectiveness and efficiency of our approach in comparison with other competing baselines models. For fairness purposes, we did not compare 1DCNN with ensemble based approaches that encapsulates many learners. Our results show that the performance of our model is superior to all other baselines pertaining to the shapelet-based classifier category, with up to 95% less Floating Points Operations per Second (FLOPs) required by the network.

In recent years, node embedding algorithms, which learn low dimensional vector representations for nodes in a graph, have been one of the key research interests of the graph mining community. The existing algorithms either rely on computationally expensive eigendecomposition of the large matrices, or require tuning of the word embedding-based hyperparameters as a result of representing the graph as a node sequence similar to the sentences in a document. Moreover, the latent features produced by these algorithms are hard to interpret. In this paper, we present Tensor Decomposition-based Node Embedding (TDNE), a novel model for learning node representations for arbitrary types of graphs: undirected, directed, and/or weighted. Our model preserves the local and global structural properties of a graph by constructing a third-order tensor using the k-step transition probability matrices and decomposing the tensor through CANDECOMP/PARAFAC (CP) decomposition in order to produce an interpretable, low dimensional vector space for the nodes. Our experimental evaluation using two well-known social network datasets proves TDNE to be interpretable with respect to the understandability of the feature space, and precise with respect to the network reconstruction.

In recent years, node embedding algorithms, which learn low dimensional vector representations for nodes in a graph, have been one of the key research interests of the graph mining community. The existing algorithms either rely on computationally expensive eigendecomposition of the large matrices, or require tuning of the word embedding-based hyperparameters as a result of representing the graph as a node sequence similar to the sentences in a document. Moreover, the latent features produced by these algorithms are hard to interpret. In this paper, we present two novel tensor decomposition-based node embedding algorithms, that can learn node features from arbitrary types of graphs: undirected, directed, and/or weighted, without relying on eigendecomposition or word embedding-based hyperparameters. Both algorithms preserve the local and global structural properties of the graph by using k-step transition probability matrices to construct third-order multidimensional arrays or tensors and perform CANDECOMP/PARAFAC (CP) decomposition in order to produce an interpretable and low dimensional vector space for the nodes. Our experiments encompass different types of graphs (undirected/directed, unweighted/weighted, sparse/dense) of different domains such as social networking and neuroscience. Our experimental evaluation proves our models to be interpretable with respect to the understandability of the feature space, precise with respect to the network reconstruction and link prediction, and accurate with respect to node classification and graph classification.

k Nearest Neighbor ( kNN) is a widely used classifier in time series data analytics due to its interpretability. kNN is often referred to as a lazy learning algorithm as it does not learn any discriminative function nor does it generate any rules from the training data. Instead, kNN classifier requires a search over all the training data for classifying a single test sample which makes it computationally demanding and hard to adopt for real world application. These applications are, sometimes, time-critical such as solar flare prediction which might have irreversible impacts on Earth. Therefore, scaling the nearest neighbors search to large datasets is crucial. In this paper, we propose a new scalable methodology to mitigate the problem of kNN high computational cost by approximating the nearest neighbor(s) with the help of clustering as a preprocessing step. We tested our idea on a comprehensive set of datasets with varying sizes and labels. Our results show that the performance of our approximate technique is comparable to the exact kNN classifier with up to 10x speed-up.

Solar flare prediction is an important task because of their potential impacts on both space and terrestrial infrastructure. This prediction task can be modeled as a binary classification between flaring and non-flaring Active Regions. Previous works on flare prediction focused on representing flaring and non-flaring Active Region examples in vector space, where the feature space was found from the Active Region magnetic field parameters. We extract time series samples of these Active Region parameters and present a flare prediction method based on the k-NN classification of the univariate time series. We find that, for our classification task, using a statistical summarization on the time series of a single Active Region parameter, called total unsigned current helicity, outperforms the use of all Active Region parameters at a single instant of time. Additionally, we present a data model of the flaring/non-flaring Active Regions using multivariate time series.

Space Weather is of rising importance in scientific discipline that describes the way in which the Sun and space impact a myriad of activities down on Earth as well as the safety of the space crew members on board of the space stations. Consequently, it is imperative to better quantify the risk of future space weather events. Most of the flare prediction models in literature use physical parameters of the potentially flaring active regions during a limited interval to gain insights on whether a flare will happen or not. This limits our perception of how an event evolves for an extended duration across multiple parameters. In this paper we followed a data-driven approach to address the problem of flare prediction from a multivariate time series analysis perspective and attempt to cluster potential flaring active regions by applying Distance Density clustering on individual parameters and further organize the clustering results into a multivariate time series decision tree. We compared different data extraction priors and spans, and ranked the importance for different parameters through univariate clustering. To the best of our knowledge, this is the first attempt to predict solar flares using a tree structure.