Summary
In this chapter, we looked at the many and interesting aspects of time series analysis in Python with Pandas and statsmodels, how they handle the data, and some of the basic manipulation functions that are available. We also looked at the concept of stationarity, how to test your time series for it, and how to transform a non-stationary series into a stationary one. You also found out the various patterns and components that time series can be built up by, and finally, we went through how to create ARIMA models and predict future values based on previous historical data.
This chapter concludes the book. We have covered many different analysis techniques and general statistical knowledge and how to use them in Python to your benefit. With ...
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