January 2020
Beginner to intermediate
432 pages
11h 24m
English
Content preview from Python for Finance Cookbook
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How it works...
Prophet was designed for analyzing time series with daily observations (which does not mean that there are no ways to use weekly or monthly data) that exhibit patterns on different time scales (weekly, monthly, yearly, and so on).
In Step 2, we downloaded daily gold prices from Quandl and created a pandas DataFrame with two columns: ds, indicating the timestamp, and y, which is the target variable. This structure (column names) is required for working with Prophet. Then, we split the DataFrame into training (years 2000-2004) and test (the year 2005) sets by slicing over time.
In Step 4, we instantiated the model with additive seasonality. Additionally, we added the monthly seasonality by using the add_seasonality method with ...
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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.