I have a signal (x-axis: Time, y-axis: Data) which has more than 10000 samples per second. The signal itself represents some physical data (e.g. speed, acceleration, etc.) and has some noise on it.
Details about the considered signal:
- The signal increases/decreases over the considered capture interval with a small gradient
- Let's consider the case where the signal decreases:
- Due to the arbitrary noise (which has a very small amplitude) it regularly happens that one (or more consecutive) samples are not smaller than their predecessor.
- However due to the high sampling rate those deviations are just an issue for the evaluation routine (Python) trying to decide whether the signals is continously decreasing in the considered time interval.
- For a human being, with optical inspection of the plot it is very clear that the signal is decreasing
What is the most robust way to determine, whether the signal is increasing or decreasing over time?
I thought about using a moving average filter but I am curious to know whether there is a more proper way to reach my goal.
Any answer is highly appreciated.