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Python for Finance Cookbook – Second Edition

You're reading from   Python for Finance Cookbook – Second Edition Over 80 powerful recipes for effective financial data analysis

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803243191
Length 740 pages
Edition 2nd Edition
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Author (1):
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Eryk Lewinson Eryk Lewinson
Author Profile Icon Eryk Lewinson
Eryk Lewinson
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Table of Contents (18) Chapters Close

Preface 1. Acquiring Financial Data FREE CHAPTER 2. Data Preprocessing 3. Visualizing Financial Time Series 4. Exploring Financial Time Series Data 5. Technical Analysis and Building Interactive Dashboards 6. Time Series Analysis and Forecasting 7. Machine Learning-Based Approaches to Time Series Forecasting 8. Multi-Factor Models 9. Modeling Volatility with GARCH Class Models 10. Monte Carlo Simulations in Finance 11. Asset Allocation 12. Backtesting Trading Strategies 13. Applied Machine Learning: Identifying Credit Default 14. Advanced Concepts for Machine Learning Projects 15. Deep Learning in Finance 16. Other Books You May Enjoy
17. Index

Correcting for stationarity in time series

In the previous recipe, we have learned how to investigate if a given time series is stationary. In this one, we investigate how to make a non-stationary time series stationary by using one (or multiple) of the following transformations:

  • deflation - accounting for inflation in monetary series using the Consumer Price Index (CPI)
  • applying the natural logarithm - making the potential exponential trend closer to linear and reducing the variance of the time series
  • differencing - taking the difference between the current observation and a lagged value (observation x time points before it)

For this exercise, we use monthly gold prices from the years 2000 to 2010. We have chosen this sample on purpose, as over that period the price of gold exhibits a consistently increasing trend - the series is definitely not stationary.

How to do it...

Execute the following steps to transform the series from non-stationary to stationary.

  1. Import the libraries...
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