arXiv (All)
open access
Rethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning
Multivariate time series forecasting (MTSF) is a fundamental task in many real world applications. Existing patch based forecasting methods generally fall into three categories: fixed partitioning, multi-scale partitioning, and extendable partitioning. Fixed partitioning often breaks meaningful temp
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
- Rethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning#471297
- Graph-Based Machine Learning for Multivariate Time Series Prediction in Scientific Domains: Streamflow Forecasting and Solar Flare Prediction#429891
- TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion#422258
- Seasonal Predictions and Their Applications in the Mediterranean Region: Part I—Sources of Predictability and Prediction Skill#137606
- DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting#471244
Record · ID 1131647
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