标题： 高性能机器学习服务的偶然或突发性生产中断预测的基础模型和随机模型评估 显示英文标题

标题： Evaluation of a Foundational Model and Stochastic Models for Forecasting Sporadic or Spiky Production Outages of High-Performance Machine Learning Services

摘要： 时间序列预测模型有广泛的实际应用（例如，从电力指标到软件工作负载）。用于时间序列预测的最新基础模型显示出优势（例如，在长序列和零样本设置中）。然而，基础模型尚未用于预测罕见的突发事件，即一个具有挑战性的目标，因为这些是极端事件的一个特殊情况。在本文中，我们将最先进的基础模型进行优化，以预测由为数十亿客户端设备提供支持的高性能机器学习服务引起的偶发或突发性停机。我们评估了基础模型与经典随机预测模型（例如，移动平均和自回归）的预测误差。分析帮助我们了解每个评估模型在偶发或突发事件中的表现。例如，它识别出目标数据中的关键模式，这些模式被基础模型与每个随机模型很好地跟踪。我们使用参数最优的模型来估计某一根本原因导致的全年停机统计数据，误差小于6%。 显示英文摘要

摘要： 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.