In this chapter, we discussed several types of data such as cross-sectional, time series, and panel data. We delved into the special properties that make time series data special. Several examples and working code in Python have been discussed to give an understanding of how exploratory data analysis can be performed on time series to visualize its properties. We also described autocorrelation and partial autocorrelation and graphical techniques to detect these in a time series. The topics discussed in this chapter give us the stage for a more detailed discussion for working on time series data in Python.
In the next chapter, you will learn how to read more complex data types in time series and use such information for more in-depth exploratory data analysis. We will revisit autocorrelation in the context of stationarity of time series. Statistical methods to detect autocorrelation would be discussed. We would also discuss importance of stationarity and describe different differencing and averaging methods for stationarizing a non-stationary time series. Additive and multiplicative models of time decomposition for estimating trend and seasonality are discussed.
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Book Overview & Buying
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Table Of Contents
Practical Time Series Analysis
By :
Practical Time Series Analysis
By:
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
