In this video we start with some data from a random process. Each piece of data represents a small time period and change in value. Think of it as something like a stock ticker, very noisy with a larger trend upward. From the patterns in this random data we can visualize it and try to predict what that phenomenon might do in the future. We will get a range of realistic futures and a sense of what the average looks like.
Polynomial fitting is problematic, so instead bootstraping of data will be tried. This week show the results, and the next two weeks show how it was done.