Introduction to Time Series Forecasting With Python

Introduction to Time Series Forecasting With Python

Discover How to Prepare Data and Develop Models to Predict the Future

Introduction to Time Series Forecasting With Python-400

$37 USD

Time series forecasting is different from other machine learning problems.

The key difference is the fixed sequence of observations and the constraints and additional structure this provides.

In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and specialized methods for time series forecasting.

Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

Technical Details About the Book

  • PDF format Ebook.
  • 8 parts, 34 chapters, 367 pages.
  • 28 step-by-step tutorial lessons.
  • 3 end-to-end projects.
  • 181 Python (.py) files.

Clear and Complete Examples.
No Math. Nothing Hidden.

Click to jump straight to the packages.

Nader Nazemi

Nothing comes close to the level of detail and practicality of these masterpieces.

Time Series Problems are Important

Time series forecasting is an important area of machine learning that is often neglected.

It is important because there are so many prediction problems that involve a time component. These problems are neglected because it is this time component that makes time series problems more difficult to handle.

You can’t just fire a machine learning algorithm at a time series dataset.

  • Time series data must be transformed into a supervised learning problem.
  • Time series data has temporal structure like trends and seasonality that must be handled.
  • Time series data has a forecast horizon.

There are a few conceptual steps you must make before you can start developing forecasting models.

There are also specialized terminology and algorithms to consider and use when working with time series data.

It can feel overwhelming for a beginner and standard machine learning libraries like scikit-learn do not make it easy to get started.

Introducing: “Time Series Forecasting With Python

This is the book I wish I had when I was getting started with univariate time series forecasting.

It is designed for the practical and hands-on way you prefer to learn.

The goal of this book is to:

Show you how to get results on univariate time series forecasting problems using the Python ecosystem.

It is a cookbook designed for immediate use.

This book was developed using five principles.

They are:

  • Application: The focus is on the application of forecasting rather than the theory.
  • Lessons: The book is broken down into short lessons, each focused on a specific topic.
  • Value: Lessons focus on the most used and most useful aspects of a forecasting project.
  • Results: Each lesson provides a path to a usable and reproducible result.
  • Speed: Each lesson is designed to provide the shortest path to a result.

These principles shape the structure and organization of the book.

What You Will Know and Be Able to Do (Reading Outcomes)

If you choose to work through all of the lessons and projects of this book, you can set some reasonable expectations on your new found capabilities.

They are:

  • Time Series Foundations: You will be able to identify time series forecasting problems as distinct from other predictive modeling problems and how time series can be framed as supervised learning.
  • Transform Data For Modeling: You will be able to transform, rescale, smooth and engineer features from time series data in order to best expose the underlying inherent properties of the problem (the signal) to learning algorithms for forecasting.
  • Harness Temporal Structure: You will be able to analyze time series data and understand the temporal structure inherent in it such as trends and seasonality and how these structures may be addressed, removed and harnessed when forecasting.
  • Evaluate Models: You will be able to devise a model test harness for a univariate forecasting problem and estimate the baseline skill and expected model performance on unseen data with various performance measures.
  • Apply Classical Methods: You will be able to select, apply and interpret the results from classical linear methods such as Autoregression, Moving Average and ARIMA models on univariate time series forecasting problems.

You will be a capable predictive modeler for univariate time series forecasting problems using the Python ecosystem.

Time Series Forecasting With Python‘ is for Python Developers…

This book makes some assumptions about you.

They are:

  • You’re a Developer: This is a book for developers. You are a developer of some sort. You know how to read and write code. You know how to develop and debug a program.
  • You know Python: This is a book for Python people. You know the Python program- ming language, or you’re a skilled enough developer that you can pick it up as you go along.
  • You know some Machine Learning: This is a book for novice machine learning practitioners. You know some basic practical machine learning, or you can figure it out quickly.

No mathematical prerequisites are needed.
No scikit-learn prerequisites are needed.

This is a playbook, a cookbook, a field guide, not a textbook for academics.

Time Series Forecasting for Beginners

It is an introductory book for time series forecasting.

As such, it focuses on univariate (one variable) data, rather than more complex multivariate problems. It also focuses on using powerful linear methods like ARIMA, rather than more exotic methods.

Everything You Need to Know to Develop Time Series Forecasting Models

You Will Get:
28 Lessons on Python Best Practices for Time Series Forecasting and
3 Project Tutorials that Tie it All Together

This Ebook was written around two themes designed to get you started and using Python for applied time series forecasting effectively and quickly.

These two parts are Lessons and Projects:

  • Lessons: Learn how the sub-tasks of time series forecasting projects map onto Python and the best practice way of working through each task.
  • Projects: Tie together all of the knowledge from the lessons by working through case study predictive modeling problems.

1. Lessons

Here is an overview of the 28 step-by-step lessons you will complete:

Each lesson was designed to be completed in about 30 minutes by the average developer.

Part I. Fundamentals

  • Python Environment
  • What is Time Series Forecasting?
  • Time Series as Supervised Learning

Part II. Data Preparation

  • Load and Explore Time Series Data
  • Data Visualization
  • Resampling and Interpolation
  • Power Transforms
  • Moving Average Smoothing

Part III. Temporal Structure

  • A Gentle Introduction to White Noise
  • A Gentle Introduction to the Random Walk
  • Decompose Time Series Data
  • Use and Remove Trends
  • Use and Remove Seasonality
  • Stationarity in Time Series Data

Part IV. Evaluate Models

  • Backtest Forecast Models
  • Forecasting Performance Measures
  • Persistence Model for Forecasting
  • Visualize Residual Forecast Errors
  • Reframe Time Series Forecasting Problems

Part V. Forecast Models

  • A Gentle Introduction to the Box-Jenkins Method
  • Autoregression Models for Forecasting
  • Moving Average Models for Forecasting
  • ARIMA Model for Forecasting
  • Autocorrelation and Partial Autocorrelation
  • Grid Search ARIMA Model Hyperparameters
  • Save Models and Make Predictions
  • Forecast Confidence Intervals

2. Projects

Here is an overview of the 3 end-to-end projects you will complete:

  • Project 1: Monthly Armed Robberies in Boston.
  • Project 2: Annual Water Usage in Baltimore.
  • Project 3: Monthly Sales of French Champagne.

Each project was designed to be completed in about 60 minutes by the average developer.

Table of Contents for Introduction to Time Series Forecasting With Python

Table of Contents for Introduction to Time Series Forecasting With Python

Take a Sneak Peek Inside The Ebook

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Introduction to Time Series Forecasting With Python Sample Page 1

Introduction to Time Series Forecasting With Python Sample Page 2

Introduction to Time Series Forecasting With Python Sample Page 3

BONUS: Time Series Forecasting Code Recipes
…you also get 181 fully working time series forecasting scripts

Sample Code Recipes

Each recipe presented in the book is standalone, meaning that you can copy and paste it into your project and use it immediately.

  • You get one Python script (.py) for each example provided in the book.
  • You get the datasets used throughout the book.

Your Time Series Code Recipe Library covers the following topics:

  • Loading data from CSV files.
  • Feature engineering.
  • Power transforms like log and sqrt.
  • Upsampling and downsampling data.
  • Interpolating missing values.
  • Moving average smoothing
  • Stationarity statistical tests.
  • Walk-forward model validation.
  • Performance measures like RMSE.
  • Naive forecast model.
  • Data visualization like line plots and ACF.
  • AR forecast models.
  • MA forecast models
  • ARIMA forecast models.
  • Grid search model parameters
  • Save forecast models to file.
  • Calculate confidence intervals.

This means that you can follow along and compare your answers to a known working implementation of each example in the provided Python files.

This helps a lot to speed up your progress when working through the details of a specific task.

Python Technical Details

This section provides some technical details about the book.

  • Python Version: You can use Python 2 or 3.
  • SciPy: You will use NumPy, Pandas and scikit-learn.
  • Statsmodels: You can use Statsmodels 0.6 or 0.8.
  • Operating System: You can use Windows, Linux or Mac OS X.
  • Editor: You can use a text editor and run example from the command line.
Bonus Python Code Provided With Introduction to Time Series Forecasting With Python

Bonus Python Code Provided With Introduction to Time Series Forecasting With Python

About The Author

Jason BrownleeHi, I'm Jason Brownlee.

I live in Australia with my wife and son and love to write and code.

I have a computer science background as well as a Masters and Ph.D. degree in Artificial Intelligence.

I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones. (yes I have written tons of code that runs operationally)

I get a lot of satisfaction helping developers get started and get really good at machine learning.

I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

I'm here to help if you ever have any questions. I want you to be awesome at machine learning.

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Check Out What Customers Are Saying:

Erich Glinker

In a few hours of reading this e-book I have learned that would have taken me weeks or months of digging around to find on the the internet.

Samih Eisa

As a beginner in time series forecasting, I found this book helpful and direct to the focal points of the subject. I liked most the projects part. The framework for the implementation was made clearly and well-organized.

Lykke Pedersen

I am just getting started on the book and I use it as a support to prepare myself for maybe starting on time series forecasting on my job. I have read the first two chapters and then jumped to the projects.

David Richards

I’ve really enjoyed this book. It’s pragmatic and doesn’t assume a lot from the reader, meaning I can make sure all of my thinking and approaches are working well and I can confidently have more junior people on my team take this up and get up to speed with time series forecasting.

One of the important things that Jason does with this book is make it accessible from a machine learning perspective, meaning we can use the tools and analysis we use on other problems with time series problems as well. Jason covers ARIMA models and similar models, but also shows the jumping off point between setting a problem up and using regular supervised learning approaches to our work. This book builds right to that crucial point.

Finally, if you haven’t followed Jason’s approach to teaching these topics, I think you’ll find their structure reassuring and accessible. The structure of each chapter makes things easy to find and use for real projects. There is always enough background information to get projects working with references to the literature if you’d like to dive deeper into algorithms or theory. I’ve found that I read Jason’s work quickly first to understand where he’s taking me and then I refer back to them while I’m working through actual projects and this book structure is ideal for this kind of practice.

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They have no deep explanations of theory, just working examples that are laser-focused on the information that you need to know to bring machine learning to your project.

My books are not for everyone, they are carefully designed for practitioners that need to get results, fast.

How are your books different from the blog?

The books are a concentrated and more convenient version of what I put on the blog.

I design my books to be a combination of lessons and projects to teach you how to use a specific machine learning tool or library and then apply it to real predictive modeling problems.

The books get updated with bug fixes, updates for API changes and the addition of new chapters, and these updates are totally free.

I do put some of the book chapters on the blog as examples, but they are not tied to the surrounding chapters or the narrative that a book offers and do not offer the standalone code files.

With each book, you also get all of the source code files used in the book that you can use as recipes to jump-start your own predictive modeling problems.

How are the 2 algorithms books different?

The book “Master Machine Learning Algorithms” is for programmers and non-programmers alike that learn through worked examples. It teaches you how 10 top machine learning algorithms work, with worked examples in arithmetic, not code (and spreadsheets) that show how each model learns and makes predictions.

The book “Machine Learning Algorithms From Scratch” is for programmers that learn by writing code to understand. It provides step-by-step tutorials on how to implement top algorithms as well as how to load data, evaluate models and more. It has less on how the algorithms work, instead focusing exclusively on how to implement each in code.

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