Time Series Forecasting in Python

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.

In Time Series Forecasting in Python you will learn how to:

    Recognize a time series forecasting problem and build a performant predictive model
    Create univariate forecasting models that account for seasonal effects and external variables
    Build multivariate forecasting models to predict many time series at once
    Leverage large datasets by using deep learning for forecasting time series
    Automate the forecasting process

Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. Youll explore interesting real-world datasets like Googles daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
You can predict the futurewith a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before.

About the book
Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, youll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and youll soon be ready to build your own accurate, insightful forecasts.

What\'s inside

    Create models for seasonal effects and external variables
    Multivariate forecasting models to predict multiple time series
    Deep learning for large datasets
    Automate the forecasting process

About the reader
For data scientists familiar with Python and TensorFlow.

About the author
Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canadas largest banks.

Table of Contents
PART 1 TIME WAITS FOR NO ONE
1 Understanding time series forecasting
2 A naive prediction of the future
3 Going on a random walk
PART 2 FORECASTING WITH STATISTICAL MODELS
4 Modeling a moving average process
5 Modeling an autoregressive process
6 Modeling complex time series
7 Forecasting non-stationary time series
8 Accounting for seasonality
9 Adding external variables to our model
10 Forecasting multiple time series
11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia
PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING
12 Introducing deep learning for time series forecasting
13 Data windowing and creating baselines for deep learning
14 Baby steps with deep learning
15 Remembering the past with LSTM
16 Filtering a time series with CNN
17 Using predictions to make more predictions
18 Capstone: Forecasting the electric power consumption of a household
PART 4 AUTOMATING FORECASTING AT SCALE
19 Automating time series forecasting with Prophet
20 Capstone: Forecasting the monthly average retail price of steak in Canada
21 Going above and beyond

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ISBN-10

161729988X

ISBN-13

978-1617299889

Publisher

Manning

Price

File Type

PDF

Page No.

456

Marco Peixeiro

Senior data scientist , author, published researcher, blogger and online instructor! I hope to inspi ...

Review

"The importance of time series analysis cannot be overstated. This book provides key techniques to deal with time series data in real-world applications. Indispensable."
Amaresh Rajasekharan, IBM

"Marco Peixeiro presents concepts clearly using interesting examples and illustrative plots. You'll be up and running quickly using the power of Python."
Ariel Andres, MD Financial Management

"What caught my attention were the practical examples immediately applicable to real life. He explains complex topics without the excess of mathematical formalism."
Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland

"A superb book on all things around modeling and predicting with time series data."
- Gary Bake, Data Scientist, Brambles

"This book will teach you everything you need to know on time series"
- David Paccoud, Principal Architect at Clario

From the Author

With this book, I hope to create the one-stop reference for time series forecasting with Python. It covers both statistical and machine learning models. We also work with automated forecasting libraries, as they are widely used in the industry and often act as baseline models. The book greatly emphasizes on a hands-on, practical approach, with various real-life scenarios. In real life, data is messy, dirty, sometimes missing, and I so I wanted to give the reader a safe space to experiment with those difficulties, learn from them, and easily transpose those skills in their own projects.
In each chapter, you will find exercises to practice and hone your skills. Each exercise comes with a full solution on GitHub. I highly suggest that you take the time to complete them, as you will gain important practical skills. It is a great way to test your knowledge, see what you need to revise in a given chapter, and apply modeling techniques in new scenarios.
Upon reading and completing the exercises, readers will have all the necessary tools to tackle any forecasting project with confidence and great results. Hopefully, they will also gain the curiosity and motivation to go beyond this book and become a time series expert.

Time Series Forecasting in Python

Python

Marco Peixeiro

Manning

Marco Peixeiro

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