Papers Time Series Classification
“Time Series Classification” 태그가 달린 논문 822편 · 필터 해제
MomentQuant: an even more minimalist interval method with linear time complexity for time series classification
Time series data is very common in many real-world applications and in numerous domains, with increasing interest for automated information extraction using machine learning. One of these subfields is time series classif…
Time Series ClassificationInformation ExtractionMulti-Class Electrical and Mechanical Fault Classification Using Random Convolutional Kernels
Diagnosing faults in rotating machinery is essential for ensuring the reliability of industrial processes. Random convolutional kernel-based Time Series Classification (TSC) methods, such as ROCKET and its variants, prov…
Time Series ClassificationComputational EfficiencyScaling Time Series Classification via XAI-Driven Data Reduction
Explainable AI (XAI) for time series has seen significant algorithmic growth, but its utility in providing measurable performance gains for downstream tasks remains under-explored. This paper bridges this gap by introduc…
Time Series ClassificationTime Series AnalysisFeature ImportanceQCNN with Rough Path Signature Kernels
Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges. A major difficulty arises from the time reparameterization invariance of t…
Time Series ClassificationBinary ClassificationTime Series AnalysisTimEE: End-to-end Time Series Classification via In-Context Learning
Time series classification (TSC) is dominated by a two-stage paradigm: train a feature encoder -- either from scratch on the target dataset or via pretraining on large corpora -- and then fit a task-specific classifier o…
Time Series ClassificationRepresentation LearningEnhancing deep learning models for time series classification via knowledge distillation
Deep learning has achieved remarkable success in various domains including time series analysis, computer vision and natural language processing. However, high computational and memory demands of state-of-the-art archite…
Time Series ClassificationKnowledge DistillationTime Series AnalysisA time-series classification framework for individual-level absenteeism prediction under severe class imbalance
Staff absenteeism imposes substantial operational costs in high-demand work environments such as healthcare, emergency services, meat processing, construction, and courier and delivery services, where proactive workforce…
Time Series ClassificationQuantum Dynamic Time Warping for Multivariate Time Series Classification
Dynamic Time Warping (DTW) is a cornerstone for time series classification, but its reliance on Euclidean distances fails to capture latent cross-channel correlations in complex multivariate data. We propose a hybrid Qua…
Time Series ClassificationBenchmarking on Tasks That Matter: Dataset Selection for Preserving Model Rankings
Benchmarks of machine learning models often include many datasets, making evaluation expensive. For efficiency, it is preferable to perform evaluations on small, representative datasets instead. The selection of such sub…
Time Series ClassificationTime Series Classification through Diffeomorphic Time Warping (DiffTW)
Time series classification involves learning a mapping from a continuous, temporally ordered sequence of real-valued observations to discrete response variables, like class labels. This task is fundamental in domains, in…
Time Series ClassificationLearning by Shifting: Temporal View Construction for Time Series Contrastive Learning
Supervised learning demands large quantities of labeled data, a bottleneck that is expensive and reliant on domain-specific expertise. Self-supervised learning, particularly contrastive learning, has emerged as a compell…
Time Series ClassificationSelf-Supervised LearningRepresentation LearningContrastive LearningMedTS-TTT: Test-Time Training for Medical Time Series Classification
Medical time series (MedTS) signals such as electroencephalography (EEG) and electrocardiography (ECG) support many clinical applications. However, substantial subject-level heterogeneity often induces subject-level dist…
Time Series ClassificationDomain AdaptationRocketPFN: Accurate Time Series Classification via In-Context Learning
We introduce RocketPFN, a training-free pipeline for time series classification that combines random convolutional feature extraction (Rocket) with in-context classification via a pretrained tabular foundation model (Tab…
Time Series ClassificationGenerating Financial Time Series by Matching Random Convolutional Features
Generating realistic financial time series is challenging as training data is often limited to a single historical path. With such scarce data, overfitting is hard to avoid, especially under adversarial training where a …
Time Series ClassificationCombining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification
Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously learnt by the classification model. This is known as class-incremental c…
Time Series ClassificationContinual LearningAnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE
Multivariate time series classification (MTSC) is pivotal in high-stakes domains, such as clinical diagnosis and industrial fault detection, where safe deployment necessitates transparent decision-making. However, isolat…
Time Series ClassificationMedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification
Medical time series are central to healthcare, enabling continuous monitoring and supporting timely clinical decisions. Despite recent progress, existing methods struggle to jointly model local-global dynamics and handle…
Time Series ClassificationGraph LearningCASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification
Multivariate time series (MTS) classification is foundational to pervasive computing and financial analysis, yet existing multi-scale paradigms are often constrained by suboptimal representation fidelity. We identify two…
Time Series ClassificationRepresentation LearningPrototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series
Time Series Classification (TSC) is a long-standing research problem that has gained increasing attention in recent years with the rapid growth of large-scale temporal data. Despite substantial progress enabled by deep l…
Time Series ClassificationChronoVAE-HOPE: Beyond Attention -- A Next-Generation VAE Foundation Model for Specialized Time Series Classification
Time Series Foundation Models (TSFMs) have become a new component of the state-of-the-art in general time series forecasting. However, adapting them to specialized classification tasks remains constrained by two intercon…
Time Series ClassificationTime Series Forecasting