Practical Time-Series Analysis

Step by Step guide filled with real world practical examples.

About This Book

• Get your first experience with data analysis with one of the most powerful types of analysis―time-series.
• Find patterns in your data and predict the future pattern based on historical data.
• Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide

Who This Book Is For

This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods.

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

In Detail

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

Table of Contents

Chapter 1. Introduction to Time Series
Chapter 2. Understanding Time Series Data
Chapter 3. Exponential Smoothing based Methods
Chapter 4. Auto-Regressive Models
Chapter 5. Deep Learning for Time Series Forecasting
Chapter 6. Getting Started with Python