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. You’ll explore interesting real-world datasets like Google’s 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.

About the technology

You can predict the future—with 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, you’ll 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 you’ll 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 Canada’s 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