Zachary Tong's article explores how moving averages, specifically through the use of pipeline aggregations in Elasticsearch, can be utilized to create dynamic control charts capable of managing complex data trends. Initially introduced with a simple dataset, the article demonstrates the adaptability of control charts to handle more challenging data scenarios, such as linear and cyclic trends. For linear trends, the dynamic nature of moving averages allows the control chart to adjust without manual modifications. In cyclic data scenarios, the article recommends using the Holt-Winters model to better account for seasonality, addressing the lag problem inherent in simpler models like EWMA. Additionally, the article explains how to configure a Watcher alert in Elasticsearch to automatically detect and notify users of data spikes, enhancing the practical application of these statistical techniques in real-time data monitoring. The piece underscores the power and versatility of moving averages not only for smoothing data but also for detecting anomalies, forecasting, and integrating alert systems directly within Elasticsearch.