arXiv:2607. 00470v1 Announce Type: cross Abstract: We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time.
By Agnieszka Kope\'c, Pawe{\l} Przyby{\l}owicz, Martyna Wi\k{a}cek
We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the model are recovered within a deep learning framework, which makes it possible to retain a transparent parametric structure while simultaneously accounting for complex and nonstationary patterns in the observed phenomenon.
arXiv:2608. 15362v1 Announce Type: cross Abstract: We propose a methodology based on the standard ReLU Deep Neural Networks (DNN) to make predictions and quantify their uncertainty.
By Kejin Wu
arXiv:2606. 01999v1 Announce Type: cross Abstract: Modern deep learning models for forecasting groups of time series rely on increasingly longer observation windows.
By Luca Butera, Giovanni De Felice, Andrea Cini, Cesare Alippi
The paper introduces a Time-Aware Bag-of-Receptive-Fields (BORF) for classifying irregular time series, extending the original BORF to handle non-uniform sampling, missing data, and variable lengths. It adds a time-weighted normalization that weights observations by their time deltas, enabling pattern extraction that reflects the true temporal distribution. The method maintains linear time complexity and is evaluated against state‑of‑the‑art irregular time‑series classifiers, achieving competitive performance while providing human‑interpretable explanations.
By Francesco Spinnato
arXiv:2512. 00239v2 Announce Type: replace Abstract: The effectiveness of self-supervised learning (SSL) for physiological time series depends on the ability of a pretraining objective to preserve information about the underlying physiological state while filtering out unrelated noise.
By Yenho Chen, Maxwell A. Xu, James M. Rehg, Christopher J. Rozell