Imagine you are planning a seaside holiday and you check the average temperatures from the past few summers. You naturally look for patterns — the slow climb into spring, the peak in July, the gentle decline into autumn. This chapter is about teaching a machine to spot those patterns automatically, and then to use them to predict the next value. We will start from the basic ideas of breaking a time series into pieces and go all the way to modern deep‑learning methods that can learn nonlinear rhythms in the data.