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1. A New Attention Mechanism to Classify Multivariate Time Series

   https://doi.org/10.24963/ijcai.2020/277  

   Hao, Yifan; Cao, Huiping (July 2020, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence)

   Classifying multivariate time series (MTS), which record the values of multiple variables over a continuous period of time, has gained a lot of attention. However, existing techniques suffer from two major issues. First, the long-range dependencies of the time-series sequences are not well captured. Second, the interactions of multiple variables are generally not represented in features. To address these aforementioned issues, we propose a novel Cross Attention Stabilized Fully Convolutional Neural Network (CA-SFCN) to classify MTS data. First, we introduce a temporal attention mechanism to extract long- and short-term memories across all time steps. Second, variable attention is designed to select relevant variables at each time step. CA-SFCN is compared with 16 approaches using 14 different MTS datasets. The extensive experimental results show that the CA-SFCN outperforms state-of-the-art classification methods, and the cross attention mechanism achieves better performance than other attention mechanisms. 

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2. Gaussian Process State-Space Models for Irregularly Sampled Sequential Data

   https://doi.org/10.1109/ICASSP55912.2026.11462443  

   Li, Ying; Lin, Zhidi; Liu, Yuhao; Zhang, Michael Minyi; Djurić, Petar M (May 2026, IEEE)

   Gaussian process state-space models (GPSSMs) provide a flexible and uncertainty-aware framework for time series analysis, but remain limited in handling irregularly sampled time series. To cope with this limitation, we propose a continuous Gaussian process state-space model (cGPSSM) that integrates a latent stochastic differential equation (SDE) prior with a Gaussian process (GP) observation model, enabling continuous-time latent evolution and principled handling of arbitrarily sampled time series. For accurate yet scalable inference, we design a learnable linear–Gaussian surrogate likelihood for GP whose posterior serves as the variational distribution, capturing temporal dependencies and enabling closed-form Kalman filtering and smoothing for efficient evidence lower bound evaluation without costly SDE solvers. Experiments on synthetic and real-world datasets demonstrate that cGPSSM consistently outperforms existing GPSSM and neural baselines in sequence reconstruction and latent trajectory estimation for both regularly and irregularly sampled time series. 

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3. One Size Doesn't Fit All: A Dynamic Heterogeneous Learning Ensemble for Malware Family Classification

   Sonya, Solomon; Zou, Muqi; Vasan, Saastha; Kruegel, Christopher; Vigna, Giovanni; Zu, Dongyan (September 2025, Springer LNCS proceedings of SECAI 2025 with the proceedings of other ESORICS 2025 Workshops)

   Malware continues to increase in prevalence and sophistication, posing significant challenges to cybersecurity. Leading cyber threat intelligence sources such as AV-TEST and VirusTotal report the discovery of over one million unique malicious files daily. Despite this staggering volume, research shows that the majority of these samples are not fundamentally novel; rather, they are variants of previously observed malware families, often exhibiting shared codebases, behavioral patterns, or structural features. In response, Artificial Intelligence (AI) models are increasingly leveraged to enhance malware classification and remediation efforts. However, while such models trained to classify malware datasets often perform well in controlled environments, research increasingly shows that conventional AI-based malware classifiers struggle to generalize to real-world, highly diverse malware datasets. We address these limitations by providing three unique contributions to the field of malware family classification.(1) We release a new benchmark dataset called MABEL: Malware Analysis BEnchmark for AI and Machine Learning. MABEL is a curated dataset containing over 82,000 labeled malware samples spanning 468 families, each described by 600+ structural, behavioral, and metadata features.(2) We introduce a novel heterogeneous ensemble with a dynamic Classification Arbiter agent that leverages the strengths of 61 diverse classifiers to improve accuracy, precision, and generalization.(3) Feedback and granular evaluation of model performance is crucial for explainability and classification optimization. This research provides enhanced classification reporting that identifies which models and features are most effective in classifying specific malware families and highlights areas for targeted model optimization. To our knowledge, this research represents one of the first to amass such a large, feature-rich dataset with malware attributed to known families and a dynamic heterogeneous ensemble that outperforms existing stateof-the-art models tested on the MABEL dataset. Furthermore, this research introduces an enhanced ensemble paradigm that can be applied to various classification domains. 

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4. Capturing Temporal Components for Time Series Classification

   Vavilthota, Venkata Ragavendra; Ramanathan, Ranjith; Aakur, Sathyanarayanan N (September 2024, Springer)

   Analyzing sequential data is crucial in many domains, particularly due to the abundance of data collected from the Internet of Things paradigm. Time series classification, the task of categorizing sequential data, has gained prominence, with machine learning approaches demonstrating remarkable performance on public benchmark datasets. However, progress has primarily been in designing architectures for learning representations from raw data at fixed (or ideal) time scales, which can fail to generalize to longer sequences. This work introduces a \textit{compositional representation learning} approach trained on statistically coherent components extracted from sequential data. Based on a multi-scale change space, an unsupervised approach is proposed to segment the sequential data into chunks with similar statistical properties. A sequence-based encoder model is trained in a multi-task setting to learn compositional representations from these temporal components for time series classification. We demonstrate its effectiveness through extensive experiments on publicly available time series classification benchmarks. Evaluating the coherence of segmented components shows its competitive performance on the unsupervised segmentation task. 

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5. A Windowed Temporal Saliency Rescaling Method for Interpreting Time Series Deep Learning Models

   Islam, Md Khairul; Fox, Judy (March 2025, AAAI'25 Workshop: AI for Time Series Analysis: Theory, Algorithms, and Applications (AI4TS))

   Song, Dongjin; Xie, Yao; Purushotham, Sanjay; Chen, Haifeng; Shen, Cong (Ed.)

   Interpreting complex time series forecasting models is challenging due to the temporal dependencies between time steps and the dynamic relevance of input features over time. Existing interpretation methods are limited by focusing mostly on classification tasks, evaluating using custom baseline models instead of the latest time series models, using simple synthetic datasets, and requiring training another model. We introduce a novel interpretation method, Windowed Temporal Saliency Rescaling (WinTSR) addressing these limitations. WinTSR explicitly captures temporal dependencies among the past time steps and efficiently scales the feature importance with this time importance. We benchmark WinTSR against 10 recent interpretation techniques with 5 state-of-the-art deep-learning models of different architectures, including a time series foundation model. We use 3 real-world datasets for both time-series classification and regression. Our comprehensive analysis shows that WinTSR significantly outranks the other local interpretation methods in overall performance. Finally, we provide a novel and open-source framework to interpret the latest time series transformers and foundation models. 

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