arXiv Machine Learning
5d ago
Adaptive $k$ Nearest Neighbors Classifier via Granular Ball Computing
arXiv:2608. 12903v1 Announce Type: new Abstract: The $k$-Nearest Neighbor~(KNN) algorithm is widely used across various tasks.
Robust and Efficient Noisy-Label Time-Series Classification via Dynamic Time Warping Based Granular Ball Computing
arXiv:2608. 11704v1 Announce Type: cross Abstract: Dynamic Time Warping (DTW)-based Nearest-Neighbor (NN) classifiers are effective for time-series classification but are vulnerable to mislabeled training samples and require numerous DTW computations during inference.
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arXiv Machine Learning
Jun 11
Efficient Time Series Clustering from Multiscale Reservoir Dynamics with Granular-Ball Anchoring Graph Optimization
arXiv:2606. 12077v1 Announce Type: new Abstract: Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency.
arXiv AI
Jul 9
TimEE: End-to-end Time Series Classification via In-Context Learning
arXiv:2607. 07500v1 Announce Type: cross Abstract: 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 on top.
arXiv AI
Jul 14
Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference
arXiv:2602. 00620v2 Announce Type: replace-cross Abstract: The zero-shot evaluation of time series foundation models (TSFMs) for classification typically uses a frozen encoder followed by a task-specific classifier.
arXiv Machine Learning
Jul 22
In-Context Time Series Classification with Random Convolutional Features
arXiv:2607. 19234v1 Announce Type: new Abstract: Time series classification is central to domains like medical signal analysis, industrial monitoring, and sensor-based activity recognition, where class information manifests as localized shapes, specific frequencies, temporal shifts, or complex cross-channel interactions.
arXiv AI
Aug 11
FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification
arXiv:2608. 08207v1 Announce Type: cross Abstract: Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient.
arXiv AI
Jun 30
Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method
arXiv:2304. 11171v5 Announce Type: replace-cross Abstract: To overcome the limitations of point-based inputs, overly fine computation and limited adaptability in existing artificial intelligence methods, Guoyin Wang and Shuyin Xia proposed granular-ball computing as a new artificial intelligence learning paradigm.
arXiv Machine Learning
Aug 11
OATS: Online Data Augmentation for Time Series Foundation Models
arXiv:2601. 19040v2 Announce Type: replace Abstract: Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality.
arXiv Machine Learning
Jun 16
Towards a Unified Generative Model for Scarce Time Series with Domain Experts
arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.
arXiv Machine Learning
Jun 9
GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
arXiv:2606. 08303v1 Announce Type: new Abstract: This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series.
arXiv Machine Learning
Jun 2
Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
arXiv:2602. 16224v2 Announce Type: replace Abstract: Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal data distribution, leading to training instability or convergence to poor local minima.
arXiv Machine Learning
Jun 10
Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection
arXiv:2606. 09874v1 Announce Type: new Abstract: Reconstruction-based methods are widely used for time series anomaly detection, where models are trained to reconstruct subsequences, and anomalies are identified through reconstruction errors.