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I am new to machine learning and I was wondering what algorithm to use to predict waveforms from an already existing waveform data samples.
I have the x coordinates and Y coordinates or (waveforms) for a few set of features for the different set of values. I want to be able to predict further waveforms for more values using this as training data. Which type of Machine Learning Algorithm should be used in such cases ?
There is no example of what kind of waveform it is in this question. So I'll answer about the waveforms I'm familiar with.
If a waveform moves with rules that are easy for humans to discover, computers can easily discover the rules too. In this case, please use RNN (Recurrent Neural Network). Among them, GRU, LSTM, etc. will be easier to access.
However, most of the reasons we want to use a computer to predict a waveform is that it is difficult for the human eye to discover the rules in waveform prediction. In this case, unfortunately, it is difficult for computers to discover rules as well.
Normality: Most statistical models were created assuming normality. Therefore, most statistical models cannot be used unless your waveform is normality. Common methods to test for normality are shapiro-wilk test and kolmogorov-smirnov test.
Stationarity: Stock prices are statistically a non-stationary time series.
2.1. Before Machine Learning: A common method to test for stationarity is the ADF test. A method of obtaining meaningful results from data input as an non-stationary time series is called 'differential'. People used ARIMA before machine learning. Therefore, If You have non-stationarity waveforms, I would recommend ARIMA first.
2.2. Machine Learning: In the case of a non-stationary time series, if the RNN is run without being differentiated, meaningless results are output. You could bring in a SOTA model, but it would still be meaningless without correction for non-stationary time series. Therefore, If You have non-stationarity waveforms and You want to solve it with machine learning, I would recommend LSTM with differentiated waveform.
If you want to know more, I recommend a book 'Practical Time Series Analysis: Prediction with Statistics and Machine Learning, Aileen Nielsen'(2019) If You show Your data, I'll edit my answer to Your data.
