WHiAR-Net: an interpretable multi-scale forecasting framework via Wavelet-Hilbert feature engineering

Kai-Cheng Wang

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DOI: 10.1038/s41598-026-54363-w
Published: 2026-05-24
Container: Scientific Reports
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-026-54363-w,
  title = {WHiAR-Net: an interpretable multi-scale forecasting framework via Wavelet-Hilbert feature engineering},
  author = {Kai-Cheng Wang},
  year = {2026},
  journal = {Scientific Reports},
  doi = {10.1038/s41598-026-54363-w},
  url = {https://doi.org/10.1038/s41598-026-54363-w}
}

RIS

TY  - JOUR
TI  - WHiAR-Net: an interpretable multi-scale forecasting framework via Wavelet-Hilbert feature engineering
AU  - Kai-Cheng Wang
PY  - 2026
JO  - Scientific Reports
DO  - 10.1038/s41598-026-54363-w
UR  - https://doi.org/10.1038/s41598-026-54363-w
ER  -

APA

Wang, K. (2026). WHiAR-Net: an interpretable multi-scale forecasting framework via Wavelet-Hilbert feature engineering. Scientific Reports. https://doi.org/10.1038/s41598-026-54363-w

Source records

crossref · retrieved 2026-09-25T22:51:03.370Z