Organisations today heavily rely on complex software systems integrated through multiple layers of middleware. This complexity leads to substantial generation of operational data of structured and semi-structured formats which is recorded in log files. The workload of the system fluctuates according to specific periods of the day which impacts the amount and quality of data generated in log files. In this paper, we propose a new log anomaly detection approach that leverages a collection of smaller models designed to capture workload fluctuations over specific time intervals. We demonstrate its effectiveness in detecting anomalies within log streams. Our evaluation uses log data from servers in a production environment, handling a complex back-end system that processes hundreds of requests per second. We show that our method outperforms traditional and widely used anomaly detection methods in data streams in the context of dynamic and time-sensitive workload scenarios.