用大模型预测高并发服务的罕见突发故障，误差低于6%。
Evaluation of a Foundational Model and Stochastic Models for Forecasting Sporadic or Spiky Production Outages of High-Performance Machine Learning Services
- 用先进基础模型优化罕见故障预测，适配长序列与零样本场景。
- 相比传统统计模型，大模型在罕见突增事件上误差更低。
- 适用于大规模服务故障预警，适合运维与系统稳定性研究者。
时间序列预测模型在电力指标、软件负载等场景中应用广泛。最新训练的时序基础模型在长序列和零样本设置下表现优异，但尚未用于预测罕见、突变型事件——这类极端事件属于典型难预测的边缘案例。本文优化了前沿基础模型，用于预测支撑数十亿客户端设备的高性能机器学习服务中罕见或突发性生产中断。通过与经典随机模型（如移动平均、自回归）对比，评估其预测误差，揭示了各模型对目标数据关键模式的捕捉能力差异。使用最优参数模型，成功估算某根因导致的一年期中断统计，误差低于6%。
原文摘要 · Abstract (English)
Time series forecasting models have diverse real world applications (e.g., from electricity metrics to software workload). Latest foundational models trained for time series forecasting show strengths (e.g., for long sequences and in zero-shot settings). However, foundational model was not yet used for forecasting rare, spiky events, i.e., a challenging target because those are a corner case of extreme events. In this paper, we optimize a state-of-the-art foundational model to forecast sporadic or spiky production outages of high-performance machine learning services powering billions of client devices. We evaluate the forecasting errors of the foundational model compared with classical stochastic forecasting models (e.g., moving average and autoregressive). The analysis helps us understand how each of the evaluated models performs for the sporadic or spiky events. For example, it identifies the key patterns in the target data that are well tracked by the foundational model vs. each of the stochastic models. We use the models with optimal parameters to estimate a year-long outage statistics of a particular root cause with less than 6% value errors.
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