Machine learning for object states

I have the objects pool with histories of their states, where each transition from one state to another takes some time, ex: $$object\ 1: A \overset{1s}{\rightarrow} B \overset{2s}{\rightarrow} C \\ object\ 2: D \overset{3s}{\rightarrow} E \overset{4s}{\rightarrow} F$$ The pool consists of many objects with right histories, where sequence of states and transition duration follows some unknown rules. Also the pool consists a small amount of object with wrong history. But which objects are right or wrong is unknown.

It needs to found mistakes in the pool. Ex, "the transition from X to Y is not allowed" or "the transition is too long for such type".

What machine learning models are more suitable for this problem?

• Welcome to DataScienceSE. Please try to give more details in your question so that we can help you: what kind of indications are available to determine if there is a mistake or not? In other words, how would a human annotator do it? Or do you mean that anything which strongly differs from the majority of the cases is a mistake? – Erwan Jul 5 '19 at 23:45
• Yes, anything which strongly differs from the majority of the cases is a mistake. I realise how to solve this with help of simple statistic, but maybe there are more suitable machine learning models. – Denis Belov Jul 6 '19 at 12:31