arXiv Machine Learning By Rahul Goswami, Shinjini Paul, Palash Ghosh, Tanujit Chakraborty

Long-Memory Reservoir Computing for Data-Scarce Dengue Forecasting

Read the original on arXiv Machine Learning →

arXiv:2607. 11272v1 Announce Type: cross Abstract: Accurate dengue forecasting is crucial for public health planning, but remains challenging because incidence series are often short, noisy, non-stationary, nonlinear, and often affected by long-range temporal dependence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 6

Echo Flow Networks

arXiv:2509. 24122v3 Announce Type: replace Abstract: At the heart of time-series forecasting (TSF) lies a fundamental challenge: how can models efficiently and effectively capture long-range temporal dependencies across ever-growing sequences?

By Hongbo Liu, Jia Xu
arXiv Machine Learning
Jun 25

Frequency Domain Reservoir Computing

arXiv:2606. 24969v1 Announce Type: new Abstract: While the quadratic sequence-length bottleneck of transformers has fueled a resurgence in recurrent models, effectively capturing complex dynamics requires architectures that balance efficient training with highly expressive latent states.

By Klaus Schertler, Xiomara Runge, Andrea Ceni, David Kappel, Claudio Gallicchio
arXiv AI
Sep 10

Adaptive Nonlinear Vector Autoregression: Robust Forecasting for Noisy Chaotic Time Series

The paper introduces a data‑adaptive nonlinear vector autoregression (NVAR) model that replaces fixed polynomial or random feature maps with a shallow, trainable multilayer perceptron (MLP). By jointly training the MLP and a linear readout via gradient‑based optimization, the model learns data‑driven nonlinearities while maintaining a simple readout structure, improving scalability in high‑dimensional settings. Experiments on several chaotic systems, both noise‑free and synthetically noisy, show that this adaptive NVAR outperforms standard NVAR, a leaky echo state network (ESN), and a hybrid ESN in predictive accuracy, demonstrating robust forecasting under noisy conditions.

By Sherkhon Azimov, Susana Lopez-Moreno, Eric Dolores-Cuenca, Sieun Lee, Jae-Il Kwon, Sangil Kim