A step-by-step example of a possibility to derive a noisy time series profile if data is scarce
Time series profiles are around us in our everyday life. There are also many specialized research works out there that deal with them.
In simple terms, a time series profile is a collection of subsequent data points y(0), y(1), … ,y(t), where one point at time t depends on the previous point at time t-1 (or even further back in time).
In many applications, one is interested in predicting how the profile behaves if some previous points are available. To do that, there are a wide variety of modeling approaches out there. In their core, the models might take some information about the past (or the present), and they give an estimation about how the profile looks in the future. One can find a lot of works that deal with such time series predictions, for example to describe weather using neural networks (Bi et al., 2023), stock price behavior via deep learning (Xiao and Su, 2022), or product demand evolution of pharmaceuticals (Rathipriya et al., 2023). Of course, those research works I just found after a quick search, so there is plenty of other things out there.