TELECOMMUNICATION NETWORK MACHINE LEARNING DATA SOURCE FAULT DETECTION AND MITIGATION

A processing system may determine a plurality of input features of a first machine learning model that is deployed in a telecommunication network for a prediction task associated with an operation of the telecommunication network and apply a time series forecast model to a historical data set of a first data source associated with at least one of the plurality of input features to generate a forecast upper bound of a first characteristic of the first data source for a first time period and a forecast lower bound of the first characteristic of the first data source for the first time period. The processing system may then detect that the first characteristic exceeds one of the forecast upper bound or the forecast lower bound during the first time period and generate an alert that an output of the first machine learning model may be faulty, in response to the detecting.