Time series profiles are around us in our on a regular basis life. There are also many specialized research works on the market that take care of them.
In easy terms, a time series profile is a set of subsequent data points y(0), y(1), … ,y(t), where one point at time t will depend on the previous point at time t-1 (and even further back in time).
In lots of applications, one is fascinated with predicting how the profile behaves if some previous points can be found. To do this, there are a wide selection of modeling approaches on the market. Of their core, the models might take some information concerning the past (or the current), they usually give an estimation about how the profile looks in the longer term. One can find a variety of works that take care of such time series predictions, for instance to explain 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). In fact, those research works I just found after a fast search, so there may be loads of other things on the market.
