Research Data Engineering

Series-wise Cross-validation for Global Time Series Forecasting

Nov 5, 2026 Source ↗

Key Insights

We consider the problem of cross-validation (CV) in time series forecasting, with a focus on global forecasting models based on deep learning.
The prevailing CV strategy in forecasting is to partition.

DataOps

Cite this synthesis DOI: 10.48550/arXiv.2611.08252 ↗

Show formatted citation

@misc{ acaciadata-research-series-wise-cross-validation-for-global-time-series-forecast,
  title = { Series-wise Cross-validation for Global Time Series Forecasting },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2611.08252 },
  url = { http://arxiv.org/abs/2611.08252v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - Series-wise Cross-validation for Global Time Series Forecasting
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2611.08252
UR  - http://arxiv.org/abs/2611.08252v1
ER  -

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Overview

We consider the problem of cross-validation (CV) in time series forecasting, with a focus on global forecasting models based on deep learning. The prevailing CV strategy in forecasting is to partition

Key Topics

Distributed Systems: A theme covered by this item within the data engineering context.

Why It Matters

This item adds to the data engineering knowledge base. Practitioners can use it to stay current on Series-wise Cross-validation for Global Time Series Forecasting, but should validate its claims against primary sources and more recent work before relying on it in production decisions.

Key Takeaways

Understand how Series-wise Cross-validation for Global Time Series Forecasting relates to data engineering workflows and controls.
Assess evidence quality and freshness before acting on the findings.
Use the tagged topics to connect this item to related content in the library.

Article Metadata

Evergreen Synthesized

distributed-systems dataops machine-learning simulation

DataOps

SQI 0.750