Methods and apparatuses for detecting and localizing faults using machine learning models

A computer implemented method of pre-processing data implemented using a processing circuitry for use in training one or more machine learning, ML, models for use in detecting anomalies occurring during execution of one or more procedures at a plurality of network nodes in a network, the method comprising:

• obtaining procedure level time series data relating to the execution of a first procedure at the plurality of network nodes;
• deriving, from the procedure level time series data relating to the execution of the first procedure at the plurality of network nodes, one or more first feature time series of one or more respective first feature values,
• wherein the one or more first feature time series are for use in training a first ML model associated with the first procedure;
• and the one or more first feature time series comprises at least one time series of a count value increase, and wherein the step of deriving one or more first feature time series comprises:
• for each event in a graph of hierarchical events, wherein the graph of hierarchical events represents the first procedure:
• determining, from the procedure level time series data relating to the execution of the first procedure at the plurality of network nodes, a time series of a count value increase for the event, wherein the count value increase comprises a difference in consecutive counts of occurrences of the event.