In recent years, there has been an increase in the use of time series records for monitoring any device with a sensor. CAN BUS records, in particularly, are essential for tracking the life cycle of off-road vehicles and other equipment. Signals of this nature are extremely sensitive to transmission errors, so they may be contaminated or missing. The solution would be to design a system that can detect and correct an anomalous message. To build this system, supervised machine learning techniques are the best option since they do not require human intervention and provide more accurate results than unsupervised methods. Although few annotated data available, and the range of reported exceptions are wide, the analyses were restricted to certain scenarios.