What might be some potential methods to encode a 100-point signal (curve) for input to a Artificial Neural Network?
Example: we have a large number of 100-pt 'curves' ranging from flat-line to approximately a half-sine wave with a wide range of max amplitude. The curves have some noise and occasional anomalous 'bumps'. These training set curves can be broken down into known positives or negatives for 'truthing'.
If I wanted to use an Artificial Neural Network to give a result for untrained data curves, what are some potential methods to encode the input data sets?
Would transforming each of the 100 points in a curve to a [0.0000 .... 1.0000] range be useful (and thus we'd have 100 inputs to the ANN? Or might another method produce better results?
NOTE: I understand that FFT and Power Spectrum analysis might be a completely different alternative here as well, but am focusing right now on a possible neural network method.