As an analytics practitioner, I frequently come across noisy data (e.g. IoT data). When building a model or machine learning algorithm, it can be advantageous to smooth this data. Over the years, I have come across a variety of smoothing techniques, but have never been able to synthesize succinctly when to use one technique over another.
I am hoping someone can shed some light on any/all of the following techniques. Even better if the response can be kept at a high-level / layman's explanation to facilitate communication to a non-technical audience.
- Fast Fourier Transform (FFT)
- LOESS / LOWESS
- Gaussian Filter
- Moving average
- Kernel Smoothing
In particular, are there conditions/situations in which one should not use one or more of the above methods? Feel free to add other methods/techniques to the arsenal.