{
  "id": 17855,
  "url": "https://link.springer.com/article/10.1007/s10462-026-11674-8",
  "title": "A survey of deep multivariate time-series models with an empirical reproducibility audit",
  "summary": "Multivariate time series (MTS) analysis is increasingly important for extracting insights from complex, interdependent temporal data in domains such as healthcare, finance, and industrial monitoring. Recent advances in deep learning have significantly improved MTS modeling; yet, the rapidly expanding literature remains fragmented across tasks, architectures, and evaluation practices. This survey concentrates on deep learning-centric approaches in MTS research across seven key tasks: classificati",
  "authors": null,
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-09T00:00:00.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "x-artificial-intelligence-review",
  "source_name": "Artificial Intelligence Review",
  "source_homepage": "https://link.springer.com/journal/10462",
  "ethics_ai_record_url": "https://ethics.ai/record/17855",
  "original_url": "https://link.springer.com/article/10.1007/s10462-026-11674-8",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}