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arXiv Machine Learning

Revisiting WEASEL 2.0: Reproduction, Sensitivity, and an Adaptive Ensemble-Size Rule

WEASEL 2.0 is a dictionary‑based time‑series classifier that uses dilated sliding windows, a randomised hyperparameter ensemble, and a fixed‑size dense feature representation. The authors reproduced the method on 114 UCR datasets, matching the original accuracy results, and examined the sensitivity of four design choices. They found that the rule for maximum ensemble size is over‑provisioned for long series and proposed an adaptive rule that reduces memory and time usage without significantly affecting accuracy.

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Oct 5

TIGER: Time-Series Classification with In-Context-Learning Gated Ensemble of Representations

TIGER is a time‑series classification method that uses a small set of three generic classifiers applied to four different representation families, producing twelve base learners. The predictions are stacked into a meta‑feature matrix and an adaptive meta‑classifier—choosing between a weighted hard majority vote and the pretrained TabICLv2 model—selects the best rule per dataset based on training sample size. On a 142‑dataset UCR benchmark, TIGER achieves the highest mean accuracy, balanced accuracy, and F1‑score among six compared algorithms, outperforming each constituent method and demonstrating strong generalization with a single hyperparameter.

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arXiv Machine Learning
Sep 18

A Network Science Approach to Granular Time Series Segmentation

The paper presents a method for dense univariate time‑series segmentation by treating each time point as a node in a graph and applying a Graph Attention Network (GAT). Using visibility‑based graph constructions, particularly WDPVG, the approach achieves competitive performance on a 59‑dataset benchmark, matching or surpassing traditional point‑wise classifiers while requiring far fewer parameters and less memory. The method’s advantage stems from long‑range edges that provide contextual information beyond local windows.

By Ivana Kesi\'c, Carolina Fortuna, Mihael Mohor\v{c}i\v{c}, Bla\v{z} Bertalani\v{c}
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arXiv Machine Learning
Sep 2

When Does Online Adaptation Pay on the Edge? A Leakage-Free Evaluation of Warmup, Learning-Rate Selection, and Resource Trade-offs for Time-Series Forecasting

The paper investigates when online adaptation benefits edge time‑series forecasting under distribution drift, using a leakage‑free streaming protocol on six public multivariate datasets. It shows that the warmup budget for static baselines and the choice of learning rate can bias perceived adaptation gains, and that a validation‑only procedure selecting warmup and optimizer rates yields Adam outperforming SGD with momentum in most settings. The study also examines accuracy versus adaptation‑state memory and per‑update latency for different adaptation strategies, highlighting parameter‑efficient variants that are nondominated on the memory axis.

By Takumi Fujimoto, Hiroaki Nishi
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arXiv Machine Learning
Sep 4

Sliding-Window Reordering with Overlap Averaging: A Simple Time-Domain Augmentation for Multivariate Forecasting

The paper introduces a simple, model‑agnostic time‑domain augmentation called Sliding‑Window Reordering with Overlap Averaging. It transforms the joint input‑target sequence into overlapping windows, randomly reorders a fraction of them based on a variance criterion, and reconstructs the sequence by averaging overlaps to generate synthetic samples with controlled variation and minimal temporal distortion. Experiments show strong performance gains across nine long‑term forecasting benchmarks and four short‑term traffic benchmarks, with detailed ablations and diagnostics highlighting the effectiveness of each design choice.

By Jafar Bakhshaliyev, Johannes Burchert, Niels Landwehr, Lars Schmidt-Thieme
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arXiv Machine Learning
Jul 2

LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning

arXiv:2607. 00958v1 Announce Type: new Abstract: Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self-supervised learning often depends on view and augmentation choices that encode domain-specific invariances.

By Alexander Chemeris, Ming Jin, Randall Balestriero
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