TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
This provides a new benchmark for observability tasks like anomaly detection and multi-modal reasoning, addressing a gap in public data for enterprises monitoring complex systems, though it is incremental as it focuses on dataset creation.
The authors tackled the lack of public observability datasets for time series and language analysis by introducing TelecomTS, a large-scale dataset from a 5G network, and found that existing models struggle with its noisy dynamics, highlighting the importance of scale information.
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 weather, observability data are zero-inflated, highly stochastic, and exhibit minimal temporal structure. Despite their importance, observability datasets are underrepresented in public benchmarks due to proprietary restrictions. Existing datasets are often anonymized and normalized, removing scale information and limiting their use for tasks beyond forecasting, such as anomaly detection, root-cause analysis, and multi-modal reasoning. To address this gap, we introduce TelecomTS, a large-scale observability dataset derived from a 5G telecommunications network. TelecomTS features heterogeneous, de-anonymized covariates with explicit scale information and supports a suite of downstream tasks, including anomaly detection, root-cause analysis, and a question-answering benchmark requiring multi-modal reasoning. Benchmarking state-of-the-art time series, language, and reasoning models reveals that existing approaches struggle with the abrupt, noisy, and high-variance dynamics of observability data. Our experiments also underscore the importance of preserving covariates' absolute scale, emphasizing the need for foundation time series models that natively leverage scale information for practical observability applications.