arXiv:2508. 16509v3 Announce Type: replace-cross Abstract: The ability to quantify information transmission is crucial for the analysis and design of both natural and engineered systems.
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.
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.
arXiv:2606. 01999v1 Announce Type: cross Abstract: Modern deep learning models for forecasting groups of time series rely on increasingly longer observation windows.
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.
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.
arXiv:2609.08219v1 Announce Type: cross Abstract: Neural networks acquire internal representations through learning. In this work, we formulate stochastic gradient descent (SGD) as a Markovian stocha...
arXiv:2606. 27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining.
arXiv:2510. 05589v3 Announce Type: replace-cross Abstract: Effective time series forecasting enables various real-world applications, benefiting from the proliferation of mobile devices.
Neural Composite Likelihood Estimation (NCLE) extends simulation‑based inference to high‑dimensional time series by partitioning long sequences into equal‑sized batches. For each batch, a neural network estimates the likelihood via conditional density estimation, and the product of these batch likelihoods forms an approximate composite likelihood. Frequentist inference is then performed by maximizing this composite likelihood to obtain a point estimate and by estimating the Godambe information matrix to derive confidence intervals.
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:2606. 00241v1 Announce Type: cross Abstract: Measuring statistical dependency between high-dimensional random variables is a fundamental task in data science and machine learning.