I have an input vector $X$, which contains a series of measurements within a period, e.g. 100 measurements in 1 sec. The goal is to predict an event, let's say, moving forward, backward or static.
I don't want to predict the output just by looking at one series of measurements, but by looking at a window of $n$ vectors $X$ of measurements, making it dependant on the previous measurements, because of the noise in the measurements.
Is there a way RNN can help me with this? Many to one architecture? LSTM? CNN of 1D + LSTM + dense?