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

2026-06-09

AI Summary

This work addresses the poor transferability of general-purpose temporal foundation models to wireless network telemetry data, which exhibit burstiness, zero-inflation, and cross-layer coupling. To overcome these challenges, the authors propose APEX—a decoder-only Transformer architecture pretrained on large-scale real-world enterprise access point (AP) telemetry data—specifically designed for edge-side forecasting and anomaly detection. The paper introduces a network-native pretraining paradigm and presents two variants: APEX-Edge, a lightweight model optimized for edge deployment, and APEX-Large, a high-performance version for cloud inference. Experimental results demonstrate that APEX-Large reduces mean absolute error (MAE) by 18% over the strongest baseline Toto (and by 38% over SARIMA) on DHCP degradation prediction, achieving an F1 score of 0.93 in anomaly detection. Meanwhile, APEX-Edge enables sub-second, privacy-preserving inference directly on AP devices.

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.

Problem

• time-series foundation model
• wireless network telemetry
• bursty signals
• zero-inflated data
• cross-layer coupling

Innovation

• network-native foundation model
• decoder-only transformer
• wireless telemetry forecasting
• edge inference
• multivariate time-series anomaly detection