We started this chapter by discussing advanced data processing techniques such as resampling, group-by, and moving window computations to obtain aggregate statistics from a time series. Next, we described stationary time series and discussed statistical tests of hypothesis such as Ljung-Box test and Augmented Dickey Fuller test to verify stationarity of a time series. Stationarizing non-stationary time series is important for time series forecasting. Therefore, we discussed two different approaches of stationarizing time series.
Firstly, the method of differencing, which covers first, second, and seasonal differencing, has been described for stationarizing a non-stationary time series. Secondly, time series decomposition using the statsmodels.tsa API for additive and multiplicative models has been discussed.
In the next chapter, we delve deeper in techniques of exponential...
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Table Of Contents
Practical Time Series Analysis
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Practical Time Series Analysis
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Overview of this book
Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python.
The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality, and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.
The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.
Table of Contents (7 chapters)
Preface
Free Chapter
Introduction to Time Series
Understanding Time Series Data
Exponential Smoothing based Methods
Auto-Regressive Models
Deep Learning for Time Series Forecasting
