An anomaly detection engine monitors network traffic to detect orders placed by users from an electronic catalog of items, aggregates data about the detected orders by time period, and analyzes the aggregated data to detect anomalies in activity levels associated with specific items in the catalog. To detect whether an anomaly exists in the activity data associated with a given item, a forecasting algorithm, such as an exponential smoothing algorithm, is used to generate an expected order volume for a current time period, and the expected order volume is compared to an actual order volume. Other criteria may also be taken into consideration. If an anomaly is detected, such as a sharp increase in the item's order volume, the anomaly detection engine generates an alert message to notify a catalog administrator, who may then determine whether the anomaly is attributable to an erroneous item description in the catalog.