The paper introduces FastCDTW, an efficient approximation for Continuous Dynamic Time Warping (CDTW). It offers two computational strategies: an exact closed‑form solution for piecewise‑linear warping functions and an unbiased Monte Carlo estimator for smooth warping functions such as those implemented by implicit neural networks. Both approaches provide gradients with respect to the warping function and the input time series, making the algorithm GPU‑friendly and suitable for integration into complex learning tasks like time‑series averaging, classification, and deep domain adaptation.
arXiv:2606. 23472v2 Announce Type: replace-cross Abstract: Time series classification involves learning a mapping from a continuous, temporally ordered sequence of real-valued observations to discrete response variables, like class labels.
The paper presents TWET, a Time‑Warping Estimation Trainable model that estimates time‑warping functions from a single observation. It frames the problem as a stationarization task in the wavelet domain and uses a hierarchical dilated convolutional architecture with a differentiable stationarity criterion for end‑to‑end training. Experiments demonstrate that TWET achieves higher deformation reconstruction accuracy and faster computation than existing methods, enabling low‑latency applications.
Time-warping estimation is a fundamental problem in signal processing with applications in bioacoustics, radar, and biomedical analysis. This paper introduces a Time-Warping Estimation Trainable (TWET...
The paper surveys continuous‑time (CT) machine learning, a framework for modeling temporal dynamics as continuous processes, especially useful when data are sampled irregularly or over long horizons. It introduces a unified taxonomy that groups major CT methods by their underlying mathematical formulations and shows how different architectural choices—such as vector‑field parameterization, stochasticity, memory mechanisms, and discretization—relate these families. The survey compares training algorithms, optimization strategies, failure modes, computational complexity, and benchmarks, reviews supporting software ecosystems, and outlines open challenges and future research directions.
arXiv:2607. 28035v1 Announce Type: new Abstract: Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing.
Continuous-time (CT) machine learning has emerged as a principled framework for modeling temporal dynamics as a continuous process, particularly when observations are sampled at arbitrary time points...
arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.
arXiv:2607. 09537v1 Announce Type: new Abstract: Time series forecasting requires models to capture diverse, often mutually exclusive, temporal dynamics, from smooth trend continuation to nonstationary drift and strict phase-aligned recurrence.
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.
GateTS introduces a streamlined univariate forecasting model that merges sparse Mixture-of-Experts computation with an attention-inspired gating mechanism, eliminating the need for auxiliary load‑balancing losses. The architecture achieves superior predictive accuracy across both short‑ and long‑term horizons, including intermittent signals, while using far fewer parameters than leading transformer models such as PatchTST. Empirical tests on diverse datasets show GateTS is computationally more efficient than LSTM, enabling cost‑effective inference for real‑world time‑series applications.
arXiv:2509. 23052v2 Announce Type: replace Abstract: We present a new meta-learning method to determine the optimal learning rate schedule for gradient descent.
The paper introduces a surrogate modeling framework that combines a Variational Autoencoder with a convolutional or graph basis to reduce dimensionality, and propagates the resulting latent vector over time using a Temporal Fusion Transformer. This approach captures both long‑range and short‑range dynamics while supporting static covariates, and it has been validated on three distinct systems, matching full simulation results at a fraction of the computational cost. The method also offers built‑in uncertainty quantification, enabling targeted experimental design and adaptability to new systems.