The paper investigates when forecasting accuracy can reliably reveal the underlying temporal structure of a time series. It shows that a small forecast margin does not automatically mean structural ambiguity and introduces a stability-based measure that assesses how well different temporal mechanisms can be distinguished given uncertainty in the selection objective. Experiments demonstrate that this stability metric better predicts when forecast-only structural selection succeeds or fails compared to relying solely on forecast margin.
By Qipeng Qian, Yuntao Qian
Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data's patterns (prior probability), collecting evidence (e.
By Google AI
A non-parametric variable selection for Structural VARs The post Granger Causal Networks and Indirect Feedback appeared first on Towards Data Science .
By Vedant Bedi
Part 1: A practitioner's walkthrough of univariate, multivariate, covariate-informed, and cold-start forecasting. The post Five Questions About Chronos-2, the Time Series Foundation Model appeared first on Towards Data Science .
By Shuai Guo
arXiv:2608.24303v1 Announce Type: new
Abstract: Transitioning from bespoke time series models towards time series foundation models changes the relationship of model and application from one-to-one t...
By Mathis Jander, Wouter van Heeswijk, Martijn Mes
How should we ensemble time-series forecasts better? The post Information Theory and Ensemble Models appeared first on Towards Data Science .
By Vedant Bedi
The article discusses how to handle uncertainty in probabilistic forecasting for physical signals, focusing on autoregressive rollout and uncertainty propagation. It is the second part of a series titled "Your Model's MSE Is Lying to You," which explores the limitations of mean squared error in evaluating predictive models. The post was published on Towards Data Science.
By Waleed Esmail
arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.
By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang
arXiv:2602. 16864v2 Announce Type: replace-cross Abstract: Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models.
By Daniel Durstewitz, Christoph J\"urgen Hemmer, Florian Hess, Charlotte Ricarda Doll, Lukas Eisenmann
The paper introduces SGA, a method for quantifying uncertainty in multi‑step forecasts from Time Series Foundation Models (TSFMs). SGA models all possible forecast branches as a directed acyclic graph, using the graph’s complexity—derived from topology and TSFM stochasticity—to bound and measure uncertainty. Experiments on 11 TSFMs across 27 datasets show that SGA outperforms existing uncertainty‑quantification methods, offers broader sampling coverage, and reveals that larger TSFMs tend to produce lower uncertainty estimates.
By Xin-Yu Hu, Shuang Liang, Cheng Feng, Shao-Qun Zhang
arXiv:2407. 00890v5 Announce Type: replace-cross Abstract: This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forecasting approaches.
By Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar
arXiv:2607. 09684v1 Announce Type: cross Abstract: Scientific Machine Learning (SciML) methods such as Neural Ordinary Differential Equations (NODEs), Physics-Informed Neural Networks (PINNs), and Universal Differential Equations (UDEs) are most effective when structural priors reflect reliable governing dynamics.
By Vrishank Sai Anand, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat