A class is a logical grouping of variables and functions. The class keyword is used in Python to define such logical groupings. A class often represents a real-life entity, for example, book, author, publishers, and so on. Entities have properties, which are represented by the variables defined in a class. Functions in a class, often referred to as methods, define how data about an instance of the entity can be captured and transformed. An instance of a class is a single realization of the entity. For example, book is an entity whereas Practical Time Series Analysis is an instance of book. To create instances, we initiate an object of a class. Object definition involves assigning values to the variables of the class through the constructor function. This job is done by the __init__
method that takes input and assigns them to class variables. The __init__
method can internally call other functions based on the logic of creating the object. Let's define a class about books...
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 (13 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Introduction to Time Series
Understanding Time Series Data
Exponential Smoothing based Methods
Auto-Regressive Models
Deep Learning for Time Series Forecasting
Customer Reviews