Abstract: Techniques for detecting and remediating anomalous intervals in time-series data of a monitored device are disclosed. A system trains a machine learning model on a combination of real data obtained from a monitoring device and false data generated by adding noise to the real data. The model predicts operating values for the device at individual intervals of a time-series data set. The system identifies anomalies in the time-series data based on differences between the predicted values and the real values. If the difference between a predicted value generate by the machine learning model and the real value exceeds a threshold, the system identifies a particular data point, such as a meter reading, as anomalous. The system ranks anomalies to perform remediation operations based on the ranking.