I have a dataset with the following datetime values of part delivery orders:
PartReplenishmentTime is the timeframe between
I have been given the task to "create a neural network with the purpose of finding anomalies in the dataset."
I am unsure about the following things:
- I know that auto-encoders are a form of NN which can be used to detect anomalies in datasets, but in all examples and papers I found, people were using auto-encoders with labled data, e.g. with images or time series data like ECGs. They trained their model with labeled data and then fed it some unlabled data to determine if it is anomalous or not.
For my understanding, the given data is unlabled, as I don't have examples of anomalous and right data.
- I don't know what to do with those arbitrary dates in this context; I thought about transforming them into timeframes like the
PartReplenishmentTime, but am unsure if a NN can make sense of it. What can I do with the given data to get good inputs for a model?
- The more I try to research the subject, the more I doubt if the task given to me is the right choice for the problem at hand. Maybe a linear regression analysis would be more sensible here?
I don't really know where to start developing a model, to be honest.