September 2015
Beginner to intermediate
608 pages
13h 43m
English
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Discrete time models
Discrete time models, such as the ones we have been looking at so far, separate time into slices at regular intervals. For us to be able to predict future values of time slices, we assume that they are dependent on past slices.
Note
Time series can also be analyzed with respect to frequency rather than time. We won't discuss frequency domain analysis in this chapter but the book's wiki at http://wiki.clojuredatascience.com contains links to further resources.
In the following, let yt denote the value of an observation at time t. The simplest time series possible would be one where the value of each time slice is the same as the one directly preceding it. The predictor for such a series would be:
This is to say that the prediction ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.