I have a dataset collected using an accelerometer. I am extracting the magnitudes from the signal to find the difference in running pattern between two different running surfaces. Will normalization help to improve my classification accuracy? In general, when should we normalize time series data? Any suggestions?
Data normalization (centering & scaling) tends to helps more with model convergence/stability when dealing with maching learning algorithms. Feeding ML algorithms input data with wildly different mean/variance can slow or prevent model convergence.
Normalizing your data is also helpful in that it will make your model results (e.g.-regression coefficients) more easily interpretable.
I wouldn't expect that normalization would significantly improve your actual model results, but it couldn't hurt!