arXiv (All)
open access
Modular Bayesian Foundation Models: Bayesian time series encoders for OOD detection, routing and forecasting
Industrial systems continuously generate time series that vary in sampling frequency, scale, dynamics, and operating regime. Consequently a single time-series foundation model may not be able to represent all regimes effectively and retraining a large model for every new dataset or regime shift is c
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection#1119234
- PyDPF: A Python Package for Differentiable Particle Filtering#1218342
- DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modeling#1118080
- Time Series Analysis#1330903
- Causal Lag Structure Discovery in Confounded Time Series via Orthogonalized Adaptive Estimation#1201631
Record · ID 1490113
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive