arXiv (All)
open access
STRIDE: State-Transition Representation via Increment Dynamics and Evolution
We introduce STRIDE (State-Transition Representation via Increment Dynamics and Evolution), which defines states through derivative fingerprints and learns local functions for state transitions (qpairs), recasting continuous forecasting as transition prediction. Trailing convolution windows estimate
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
- ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models#444599
- Quanta Extraction via Qualia Equation: Making Latent Data Computable in AI through Load Minimization#105602
- One Patch, Three Roles: What Is Actually Coupled in Autoregressive Time-Series Forecasting?#1049422
- Integrated spatial-temporal feature alignment with graph convolutional and gated recurrent networks for traffic flow prediction.#143138
- Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage#163174
Record · ID 1116698
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