Gegevenssmoothing
Gegevenssmoothing, also known as data smoothing, is a process used to reduce noise or fluctuations in a dataset. The goal is to reveal underlying trends or patterns that might be obscured by random variations. This is particularly useful when dealing with time-series data, but can be applied to any set of measurements.
Common techniques for data smoothing include moving averages, exponential smoothing, and polynomial fitting. A simple moving
The choice of smoothing technique and its parameters depends on the nature of the data and the