Abstract: Methods and systems are described for anomaly detection in refrigeration systems. A process for providing anomaly detection for refrigeration systems includes receiving telemetry data of one or more refrigeration systems, including measured temperature values and setpoint temperature values; processing the telemetry data to determine machine learning input data based at least in part on at least a portion of the measured temperature values and at least a portion of the setpoint temperature values; and using one or more hardware processors to apply the machine learning input data to a trained anomaly detection machine learning model to determine periodic anomaly metrics. The process provides an automatically determined indication based at least in part on at least a portion of the periodic anomaly metrics.