AutoMixer：改进业务与IT可观测性数据上的多变量时间序列预测
AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
AI总结：
针对BizITObs多变量时序中有用与噪声通道交互难以解耦的问题，提出结合AutoEncoder通道压缩预训练与TSMixer微调的AutoMixer基础模型，将Biz-KPI预测准确率持续提升11%至15%。
摘要：
业务流程的效率依赖于业务关键绩效指标（Biz-KPI），而IT故障可能对其造成负面影响。业务与IT可观测性（BizITObs）数据将Biz-KPI和IT事件通道融合为多变量时间序列数据。提前预测Biz-KPI可以通过主动纠正措施提高效率和收入。然而，BizITObs数据通常在Biz-KPI与IT事件之间同时呈现有用和含噪的通道间交互，需要有效解耦。这导致现有多变量预测模型在应用时预测性能欠佳。为解决该问题，我们提出AutoMixer，这是一种时间序列基础模型（FM）方法，基于通道压缩预训练和微调工作流这一新技术。AutoMixer利用AutoEncoder进行通道压缩预训练，并将其与先进的TSMixer模型集成，用于多变量时间序列预测。这种融合显著增强了TSMixer实现准确预测的能力，并且能够很好地泛化到多个下游任务。通过详细实验和仪表板分析，我们展示了AutoMixer能够持续将Biz-KPI的预测准确率提高11%至15%，这直接转化为可执行的业务洞察。
英文摘要：
The efficiency of business processes relies on business key performance indicators (Biz-KPIs), that can be negatively impacted by IT failures. Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data. Forecasting Biz-KPIs in advance can enhance efficiency and revenue through proactive corrective measures. However, BizITObs data generally exhibit both useful and noisy inter-channel interactions between Biz-KPIs and IT events that need to be effectively decoupled. This leads to suboptimal forecasting performance when existing multivariate forecasting models are employed. To address this, we introduce AutoMixer, a time-series Foundation Model (FM) approach, grounded on the novel technique of channel-compressed pretrain and finetune workflows. AutoMixer leverages an AutoEncoder for channel-compressed pretraining and integrates it with the advanced TSMixer model for multivariate time series forecasting. This fusion greatly enhances the potency of TSMixer for accurate forecasts and also generalizes well across several downstream tasks. Through detailed experiments and dashboard analytics, we show AutoMixer's capability to consistently improve the Biz-KPI's forecasting accuracy (by 11-15\%) which directly translates to actionable business insights.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
