I have a large dataset of signals (composed of time series). All time series describe the same process, but each series has a different duration (number of points). Based on these time series, I want to train some neural network, so that then I give a new time series as input and it predicts 100 further points. I have two questions:
- What transformations are there to reduce all signals to one size?
- What are the methods for solving such problems (predict time series)? I know the popular ARMA and ARIMA models, but they work with the same time series. The goal of my task is to find patterns between time series in order to learn how to predict the further behavior of a new time series. Thanks for any help!