The article by Camilla Montonen explains the concept of multi-bucket impact anomalies in Elastic's machine learning features, which are marked by crosses instead of circles in anomaly detection results. These anomalies arise not from a single anomalous value but from a sequence of values across multiple buckets, indicating an unusual region in the dataset's history. The article highlights their usefulness in detecting anomalies over longer time frames, as opposed to single bucket anomalies, by analyzing examples like server request data that show periodic patterns. It discusses how multi-bucket impact anomalies can occur within typical model bounds and examines how sided detectors, like high_count and low_count, interact with these anomalies. Additionally, it explains how these anomalies are scored using multi-bucket impact values, which are independent of anomaly scores, providing a broader view of data anomalies. Lastly, the article encourages readers to explore Elastic's documentation and offers a free trial for hands-on experience.