This report describes the development of an algorithm to detect anomalies in the time series from inductance loop sensors. The algorithm uses a statistic produced with inductance loop data to make an optimal prediction of the volume and occupancy values that will occur at the next time step. Anomaly detection is accomplished by applying thresholds to the difference between the predictions and the observed values. The report demonstrates the use of the anomaly detection algorithm with inductance loop data gathered on Interstate 5 in Seattle, Washington. The report also discusses the scaling and values of threshold necessary for anomaly detection.