arXiv Machine Learning
Predictability of El Ni\~no from Delayed Observations
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Sep 10
Memory in Deep Time-Series Models
arXiv:2609.06006v1 Announce Type: cross Abstract: Deep learning for time series has progressed through successive architectural paradigms, from recurrent networks and transformers to structured state...
arXiv AI
Jul 7
KAN vs LSTM Performance in Time Series Forecasting
arXiv:2511. 18613v2 Announce Type: replace-cross Abstract: This study presents a controlled comparison of baseline Kolmogorov-Arnold Networks (KAN), implemented via PyKAN, and Long Short-Term Memory (LSTM) networks for the forecasting of stochastic, non-stationary financial time series.
arXiv Machine Learning
Jul 17
GAttNHP: Group Attention Neural Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs
arXiv:2607. 14733v1 Announce Type: new Abstract: Temporal Knowledge Graphs (TKGs) record how facts evolve over time, but forecasting future events on a TKG remains difficult for three reasons: (i) long-range temporal dependencies are hard to encode; (ii) events on different chains mutually excite or inhibit one another in ways that snapshot-level models cannot express; and (iii) inter-arrival times are heavy-tailed and statistically sparse, so deterministic time predictors are unreliable.
arXiv AI
Jul 23
Challenges of Explainability in Continual Learning for Time Series Forecasting
arXiv:2607. 19382v1 Announce Type: cross Abstract: Deep learning models have shown strong potential for time series forecasting, yet their deployment in real-world environmental monitoring remains challenging due to non-stationary dynamics and limited explainability.
arXiv AI
Aug 10
Seeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gains
arXiv:2603. 15506v2 Announce Type: replace-cross Abstract: We argue that the current practice of evaluating AI/ML time-series forecasting models, predominantly on benchmarks characterized by strong, persistent periodicities and seasonalities, obscures real progress by overlooking the performance of efficient classical methods.
arXiv Machine Learning
Jul 20
SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction
arXiv:2602. 04643v2 Announce Type: replace Abstract: Time-series anomaly prediction aims to forecast future system failures before they fully emerge, making latent predictive models such as JEPA a promising framework for capturing precursor dynamics.