Practical Time Series Analysis

Step by Step guide filled with real world practical examples.
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Practical Time Series Analysis

Dr. Avishek Pal, Dr. PKS Prakash

3 customer reviews
Step by Step guide filled with real world practical examples.

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Book Details

ISBN 139781788290227
Paperback244 pages

Book Description

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

Chapter 1: Introduction to Time Series
Different types of data
Internal structures of time series
Models for time series analysis
Autocorrelation and Partial autocorrelation
Summary
Chapter 2: Understanding Time Series Data
Advanced processing and visualization of time series data
Resampling time series data
Stationary processes
Time series decomposition
Summary
Chapter 3: Exponential Smoothing based Methods
Introduction to time-series smoothing
First order exponential smoothing
Second order exponential smoothing
Modeling higher-order exponential smoothing
Summary
Chapter 4: Auto-Regressive Models
Auto-regressive models
Moving average models
Summary
Chapter 5: Deep Learning for Time Series Forecasting
Multi-layer perceptrons
Recurrent neural networks
Convolutional neural networks
Summary
Chapter 6: Getting Started with Python
Installation
Basic data types
Keywords and functions
Iterators, iterables, and generators
Classes and objects
Summary

What You Will Learn

  • Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project
  • Develop an understanding of loading, exploring, and visualizing time-series data
  • Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series
  • Take advantage of exponential smoothing to tackle noise in time series data
  • Learn how to use auto-regressive models to make predictions using time-series data
  • Build predictive models on time series using techniques based on auto-regressive moving averages
  • Discover recent advancements in deep learning to build accurate forecasting models for time series
  • Gain familiarity with the basics of Python as a powerful yet simple to write programming language

Authors

Table of Contents

Chapter 1: Introduction to Time Series
Different types of data
Internal structures of time series
Models for time series analysis
Autocorrelation and Partial autocorrelation
Summary
Chapter 2: Understanding Time Series Data
Advanced processing and visualization of time series data
Resampling time series data
Stationary processes
Time series decomposition
Summary
Chapter 3: Exponential Smoothing based Methods
Introduction to time-series smoothing
First order exponential smoothing
Second order exponential smoothing
Modeling higher-order exponential smoothing
Summary
Chapter 4: Auto-Regressive Models
Auto-regressive models
Moving average models
Summary
Chapter 5: Deep Learning for Time Series Forecasting
Multi-layer perceptrons
Recurrent neural networks
Convolutional neural networks
Summary
Chapter 6: Getting Started with Python
Installation
Basic data types
Keywords and functions
Iterators, iterables, and generators
Classes and objects
Summary

Book Details

ISBN 139781788290227
Paperback244 pages
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