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Recently web applications have been widely used in enterprises to assistemployees in providing effective and efficient business processes. Forecastingupcoming web events in enterprise web applications can be beneficial in manyways, such as efficient caching and recommendation. In this paper, we present aweb event forecasting approach, DeepEvent, in enterprise web applications forbetter anomaly detection. DeepEvent includes three key features: web-specificneural networks to take into account the characteristics of sequential webevents, self-supervised learning techniques to overcome the scarcity of labeleddata, and sequence embedding techniques to integrate contextual events andcapture dependencies among web events. We evaluate DeepEvent on web eventscollected from six real-world enterprise web applications. Our experimentalresults demonstrate that DeepEvent is effective in forecasting sequential webevents and detecting web based anomalies. DeepEvent provides a context-basedsystem for researchers and practitioners to better forecast web events withsituational awareness.

Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks