Context-Aware Diffusion for Telemetry Time Series with Permutation-Stable Feature Modeling (Student Abstract)

We present a context-aware diffusion model for multivariate

time series generation in dynamic and partially observed

environments, with applications to data-center computing

node's telemetry and beyond. The model integrates

pretrained textual embeddings to represent feature

semantics, enabling flexible, context-guided generation and

improved adaptability to unseen or re-ordered input

features. Built on a transformer architecture, it employs

both time-wise and feature-wise masking to support missing

data during training and inference. We show that the model

is robust to permutations with respect to the feature

dimension, mantaining stable performance in settings where

input configurations vary. Empirical evaluations on HPC

sensor data illustrate the model’s versatility across

generation and imputation tasks. This work introduces a

modular and generalizable framework for time series

modeling in complex, high-dimensional systems which can

serve as a digital-twin for data-center's compute node

telemetry.