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New TelecomTS dataset challenges AI models with noisy observability data

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced TelecomTS, a new large-scale dataset designed to improve the analysis of time series and language data from telecommunications networks. This dataset addresses the limitations of existing observability data by including heterogeneous, de-anonymized covariates with explicit scale information, which is crucial for tasks like anomaly detection and root cause analysis. Initial benchmarking of current state-of-the-art models revealed significant challenges in handling the noisy and high-variance dynamics present in this type of data, highlighting the need for foundation models that can effectively leverage scale information. AI

IMPACT Provides a new benchmark for time series and language models, potentially improving AI's ability to analyze complex operational data.

RANK_REASON The cluster contains an academic paper introducing a new dataset for AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New TelecomTS dataset challenges AI models with noisy observability data

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The cluster contains an academic paper introducing a new dataset for AI research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Austin Feng, Andreas Varvarigos, Ioannis Panitsas, Daniela Fernandez, Jinbiao Wei, Yuwei Guo, Jialin Chen, Ali Maatouk, Leandros Tassiulas, Rex Ying ·

    TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis

    arXiv:2510.06063v2 Announce Type: replace Abstract: Modern enterprises generate vast streams of time series metrics when monitoring complex systems, known as observability data. Unlike conventional time series from domains such as climate, observability data are zero-inflated, hi…

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