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I have chemical plant data where the product is manufactured in batches. Each batch takes about 4 hours and I have data for every 5 mins. My objective is to classify the batches as good and bad. How can I achieve this for a time related data like this one?

One method I have read is time unfolding where each time interval in combination with the feature becomes an individual feature (e:g if there is interval T0 and T1, features F0 and F1 then we will have 4 features T0 F0 and T1 F1) - please correct me if I am wrong about unfolding.

So basically we are converting each batch of N rows of data into single row. Is there any other method for this?

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  • $\begingroup$ Hi! You can look at my paper ceur-ws.org/Vol-2322/dsi4-1.pdf - section Related Work. There are a few approaches and software libraries to feature extraction from time-series data. $\endgroup$ – wind Apr 29 '19 at 8:46
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I am not clear on how you convert your N rows into a single row. Do you concatenate all of them? This is an option but could become problematic over longer sequence lengths.

A straightforward method could be to simply average the features across time-steps. Although you lose causal information, this has been shown to work surprisingly well for word embeddings.

Another standard approach is to use hidden markov models. Very simply, a hidden markov model assumes a latent state space which generates an output (in this case your feature) at every time-step conditioned on the previous state. https://en.wikipedia.org/wiki/Hidden_Markov_model

Today the practice is to use sequence neural models such as LSTMs and GRUs which can hold memory over longer time-steps. https://en.wikipedia.org/wiki/Long_short-term_memory

I suggest your read up on these methods and select the one that suits your purpose :)

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  • $\begingroup$ -Hi, here is an example for converting N rows into one row. Lets say there are 5 features F0-F4 and 10 time intervals T0-T9. Since data changes at every time interval, we can consider each time interval in combination with a feature to be a new feature. so 10 rows can be converted into 10*5 features(columns) and one single row (each feature in combination with each time interval), hope this makes sense. Taking an average is a good idea but as you pointed out, we will loose lot of data, I will have a look at Markov, thanks. Any other idea? $\endgroup$ – KarthikKulkarni May 6 '19 at 10:29

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