arXiv (All)
open access
Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes
Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability is known. Mainstream deep-learning models for this task are discriminative and deterministic: they emit a single next activity and a single remainin
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
- Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models#973692
- Feature selection methods on deep learning algorithms for stock price forecasting: A systematic literature review#1136576
- GNet: A scalable and flexible Gaussian process network with nonparametric neurons#1050914
- Task-Parametrized dynamics: Representation of time and decisions in recurrent neural networks#484921
- Sequential solution for DSGE models with deep neural networks#226496
Record · ID 1035786
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