We introduce the Cisco Time Series Model, a univariate zero-shot forecaster. This time series foundation model is the result of a general architectural innovation to a time series model enabling it to accept multiresolution input, applied to a popular decoder-only time series model (TimesFM). The resulting multiresolution decoder-only model is trained on over 300B unique data points, with more than half coming from the observability domain. Quantitative and qualitative evaluations demonstrate that the resulting model achieves superior performance on observability datasets while retaining very similar performance on a standard general-purpose forecasting benchmark (GIFT-Eval), and suggest that the multiresolution structure enables the model to make more accurate predictions on long context input.

翻译：本文介绍了Cisco时间序列模型，一种单变量零样本预测器。该时间序列基础模型源于对现有时间序列模型的通用架构创新，使其能够接受多分辨率输入，并应用于一种流行的仅解码器时间序列模型（TimesFM）。所得的多分辨率仅解码器模型在超过3000亿个独特数据点上进行训练，其中超过一半数据来源于可观测性领域。定量与定性评估表明，该模型在可观测性数据集上实现了卓越性能，同时在标准通用预测基准（GIFT-Eval）上保持了高度相似的性能，并表明多分辨率结构使模型能够在长上下文输入中做出更准确的预测。