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I am working on a time series data for which I intend to impliment machine learning model for detecting change point in time series data.

This data is recorded fom machinary and we have to predict process is completed. Now we know when process is completed(actual time at which process is getting completed) and we have to link it to the data that we have recorded from sensors. Our goal is to build model trained on this currently available historical data which will be able to predict the time at which process is getting completed for the future data.

Now my question is I know that process gets completed at 11:50, so I have created target variable which is encoded as 0 from the beginning and then 1 11:50 onwards. This data I fed to Neural networks to predict the variable. Is this a correct approach? Is there any sophisticate method for encoding the target variable than just labelling it as 1 after certain timestamp as it is time series data and my goal is to predict time at which process will get completed for the next sesnor data?

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This sounds as a supervised change point detection task. I believe your approach with the 0/1 dummy variable is fine. Alternatively to the neural network approach you could try out some classification algorithms. You might find helpful this review on change point detection (A Survey of Methods for Time Series Change Point Detection), where some alternative classifiers are suggested for the task (see "Supervised Methods" subsection).

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