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arXiv Machine Learning By Guoxiong Long, Huizhen Huang, Qikun Cai, Tao Huang, Chen Hou

Evaluating Sequence Assembly Strategies for Differentially Private Synthetic Time-Series Forecasting

Read the original on arXiv Machine Learning →

The paper investigates how post‑generation assembly of fixed‑length synthetic windows affects downstream forecasting performance. By varying overlap rates and window‑weighting schemes, the authors evaluate boundary continuity, statistical and temporal fidelity, and TSTR utility across four public datasets and five forecasting models. Results show that optimal assembly depends on the forecaster, with increased overlap improving continuity but not always reducing forecasting error, and provide assembly grids and TRTR references to quantify these effects.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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