SyntheITS: Synthetic industrial time-series data with prior knowledge and deep generative models for equipment anomaly detection under small samples

@article{xiaoqiao2026,
  title        = {{SyntheITS: Synthetic industrial time-series data with prior knowledge and deep generative models for equipment anomaly detection under small samples}},
  author       = {Xiaoqiao Wang et al.},
  journal      = {Computers in Industry},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.compind.2026.104460},
}

TY  - JOUR
TI  - SyntheITS: Synthetic industrial time-series data with prior knowledge and deep generative models for equipment anomaly detection under small samples
AU  - al., Xiaoqiao Wang et
JO  - Computers in Industry
PY  - 2026
ER  -

Xiaoqiao Wang et al. (2026). SyntheITS: Synthetic industrial time-series data with prior knowledge and deep generative models for equipment anomaly detection under small samples. *Computers in Industry*. https://doi.org/https://doi.org/10.1016/j.compind.2026.104460

Xiaoqiao Wang et al.. "SyntheITS: Synthetic industrial time-series data with prior knowledge and deep generative models for equipment anomaly detection under small samples." *Computers in Industry* (2026). https://doi.org/https://doi.org/10.1016/j.compind.2026.104460.

SyntheITS: Synthetic industrial time-series data with prior knowledge and deep generative models for equipment anomaly detection under small samples
Xiaoqiao Wang et al. · Computers in Industry · 2026
https://doi.org/https://doi.org/10.1016/j.compind.2026.104460