APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations

Abstract:Generic time-series foundation models transfer poorly to wireless network telemetry whose signals are bursty, zero-inflated, and coupled across protocol layers. We present APEX, a network-native, decoder-only transformer for forecasting enterprise AP telemetry, and evaluate it on DHCP degradation as a representative network task. APEX is pre-trained on 10-channel multivariate telemetry from ~4,500 production wireless networks (~100K AP time series, 34 metrics per AP), and is available as APEX-Large (269M, cloud) and APEX-Edge (10.5M, edge). On a 192-step (4-day) DHCP degradation benchmark, APEX-Large reduces MAE by 18% over the strongest foundation-model baseline (Toto) and 38% over SARIMA, with anomaly-detection F1 = 0.93, while APEX-Edge enables sub-second, privacy-preserving inference on AP-class edge hardware. These results suggest network-native pre-training is a practical foundation for proactive wireless operations.

5 pages, 1 figure, 4 tables. Discusses a network-native time-series foundation model for wireless edge operations

Machine Learning (cs.LG)

C.2.3; I.2.6; C.2.1

arXiv:2606.11553 [cs.LG]

(or arXiv:2606.11553v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.11553 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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