基于多分辨率输入的时序预测模型，显著提升可观测性数据预测精度。
Cisco Time Series Model Technical Report
- 采用多分辨率输入结构改进时序模型架构
- 在3000亿+数据点上训练，半数来自可观测性领域
- 长上下文预测更准确，适合运维与系统监控场景
我们提出Cisco时间序列模型，一种单变量零样本时间序列预测模型。该模型通过通用架构创新，使时间序列模型能够接受多分辨率输入，并应用于流行的仅解码器结构时间序列模型（TimesFM）。由此产生的多分辨率仅解码器模型在超过3000亿个唯一数据点上训练，其中一半以上来自可观测性领域。定量与定性评估表明，该模型在可观测性数据集上表现优异，同时在标准通用预测基准GIFT-Eval上保持相近性能，且多分辨率结构有助于提升长上下文输入下的预测准确性。
原文摘要 · Abstract (English)
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.
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