The efficiency of business processes relies on business key performance indicators (Biz-KPIs), that can be negatively impacted by IT failures. BizITOps 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, BizITOps 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.

翻译：业务流程的效率依赖于业务关键绩效指标（Biz-KPIs），而IT故障可能对其产生负面影响。BizITOps数据将Biz-KPIs与IT事件通道融合为多变量时间序列数据。通过主动纠正措施提前预测Biz-KPIs可提升效率与收益。然而，BizITOps数据通常同时存在Biz-KPIs与IT事件之间的有效和噪声通道间交互，需有效解耦。这导致现有多变量预测模型应用时性能欠佳。为此，我们提出AutoMixer——一种基于通道压缩预训练与微调工作流这一创新技术的时间序列基础模型（FM）方法。AutoMixer利用自编码器进行通道压缩预训练，并将其与先进的多变量时间序列预测模型TSMixer集成。该融合显著增强了TSMixer的精准预测能力，并在多项下游任务中展现出良好泛化性。通过详细实验与仪表盘分析，我们展示了AutoMixer持续提升Biz-KPI预测精度（11-15%）的能力，这直接转化为可操作的商业洞察。