The simplest most important idea for time series forecasting The post Measuring Structure Stability of Econometric Models appeared first on Towards Data Science .
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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.
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.
A non-parametric variable selection for Structural VARs The post Granger Causal Networks and Indirect Feedback appeared first on Towards Data Science .
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 .
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...
How should we ensemble time-series forecasts better? The post Information Theory and Ensemble Models appeared first on Towards Data Science .