I created an ML model to classify five IoT signals (say A, B, C, D, and E) I get in CSV files monthly. Each signal has a value in the sampled timestamps.
My questions (doubts) are:
- Do I have to preprocess new data in the production only on the same (in this example, daily) timestamp; in other words, only the same number of values (features) for each time-series sample as during the model's training? I am pretty sure that is true, but I wonder if there is something specific to the time series.
- Since my data are normalized and standardized, what would be the suggestion regarding the length of the time series, since that is important for the standardization of input data in the model in the production environment?
During the training, I divided the values on the daily time stamp (say 5000 values for each signal in a day). So, my time-series are a daily basis time-stamp. I have finished the training, and the results on the test dataset and with cross-validation are acceptable for production. However, I would like not to make a mistake in directions for the data acquiring team.