April 2019
Intermediate to advanced
426 pages
11h 13m
English
Content preview from Mastering Python for Finance - Second Edition
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Making a time series stationary
A non-stationary time series data is likely to be affected by a trend or seasonality. Trending time series data has a mean that is not constant over time. Data that is affected by seasonality have variations at specific intervals in time. In making a time series data stationary, the trend and seasonality effects have to be removed. Detrending, differencing, and decomposition are such methods. The resulting stationary data is then suitable for statistical forecasting. Let's look at all three methods in detail.
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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.