Performing anomalie detection on a battery volatge using LSTM-RNN

I will use the "date" and "time" columns to pre-process your data and to construct your neural net input.

RNN does not work well for very long-term dependancies... so, for example, creating a time series with all minutes in a month, won't probably work.

You must select:

• How many samples your input data will have
• What is your sampling period (minute, hour, day, week...)
• What do you want to detect: If the input data contains an anomally or you might want to predict if an anomally will occur in the future...

When you have all of this, and perhaps something else, defined you will have to use your "date" and "time" columns to create the dataset (the time-series)

Furthermore, I don't know if all batteries are checked under the same circunstances, for instance, are all of them new stock ones? If not, you can also use those columns to compute battery age or "working time" as an extra feature.