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.

翻译：业务流程的效率依赖于业务关键绩效指标（Biz-KPI），而IT故障可能对其产生负面影响。业务与IT可观测数据将Biz-KPI与IT事件通道融合为多变量时间序列数据。提前预测Biz-KPI可通过主动纠正措施提升效率与收益。然而，业务与IT可观测数据普遍存在Biz-KPI与IT事件之间既有用又嘈杂的通道间交互作用，需有效解耦。这导致现有多变量预测模型的应用效果欠佳。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的精准预测能力，并在多个下游任务中表现出良好的泛化性。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（提升11%-15%）的能力，这直接转化为可操作的业务洞察。