January 2020
Beginner to intermediate
432 pages
11h 24m
English
Content preview from Python for Finance Cookbook
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How it works...
In Step 2, we obtained forecasts from the models that were fitted using statsmodels. To do so, we used the forecast method of the fitted object. We specified the length of the forecast (by default, it is 1 period ahead). The resulting object contained the forecasts, standard deviations, and confidence intervals (using the default significance level of 5%). For simplicity, we stored the necessary information in a pandas DataFrame.
In Step 3, we worked with the auto-ARIMA model from the pmdarima library. We used the predict method and also specified the number of periods that we wanted to forecast for.
Finally, we plotted all the results, along with the Google's stock prices.
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