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Practical Time Series Analysis

By : Avishek Pal, PKS Prakash

1.4 (5)

Buy this Book

Practical Time Series Analysis

1.4 (5)

By: Avishek Pal, PKS Prakash

Buy this Book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Introduction to Time Series

Different types of data

Internal structures of time series

Models for time series analysis

Autocorrelation and Partial autocorrelation

Summary

Understanding Time Series Data

Advanced processing and visualization of time series data

Resampling time series data

Stationary processes

Time series decomposition

Summary

Exponential Smoothing based Methods

Introduction to time series smoothing

First order exponential smoothing

Second order exponential smoothing

Modeling higher-order exponential smoothing

Summary

Auto-Regressive Models

Auto-regressive models

Moving average models

Summary

Deep Learning for Time Series Forecasting

Multi-layer perceptrons

Recurrent neural networks

Convolutional neural networks

Summary

Getting Started with Python

Installation

Basic data types

Keywords and functions

Iterators, iterables, and generators

Classes and objects

Summary

Getting Started with Python

As you have chosen to read this book, we think that you might have a working knowledge of Python-if not, a hands-on expert who happens to live and breathe Python. In case you have a fair knowledge of Python at the least, you may choose to skip this appendix. If you are new to Python or looking for how to get started with the programming language, reading this appendix will help you get through the initial hurdles. It would also get you what you need to enjoy this book's chapters. So, without further ado, let's jump in!

Python is a general-purpose, high-level, and interpreted programming language, which appeared in 1991. Its creator, Guido van Rossum, started writing the interpretation of the language over the Christmas of 1989 and named the language after one of his favorite TV shows-Monty Python's Flying Circus.

Python emphasizes code...

Tech Concepts

Programming languages

Tech Tools