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Python for Finance Cookbook

You're reading from  Python for Finance Cookbook

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781789618518
Pages 432 pages
Edition 1st Edition
Languages
Author (1):
Eryk Lewinson Eryk Lewinson
Profile icon Eryk Lewinson

Table of Contents (12) Chapters

Preface 1. Financial Data and Preprocessing 2. Technical Analysis in Python 3. Time Series Modeling 4. Multi-Factor Models 5. Modeling Volatility with GARCH Class Models 6. Monte Carlo Simulations in Finance 7. Asset Allocation in Python 8. Identifying Credit Default with Machine Learning 9. Advanced Machine Learning Models in Finance 10. Deep Learning in Finance 11. Other Books You May Enjoy

Forecasting using ARIMA class models

In this recipe, we focus on using the ARIMA class models to forecast future observations of a given time series.

We compare the forecasting performance of the models we built in the Modeling time series with ARIMA class models recipe, where we investigated Google's stock prices in 2015-2018. We manually selected an ARIMA(2,1,1) model, while auto_arima suggested ARIMA(3,1,2). In this recipe, we use both models as they were initially estimated using different libraries, offering slightly different possibilities in terms of forecasting.

We forecast Google's weekly stock prices over the first 3 months of 2019.

Getting ready

The ARIMA(2,1,1) model from the Modeling time series with...

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