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:
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
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.
Quotes
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
Table of Contents
- inside front cover
- Time Series Forecasting in Python
- Copyright
- dedication
- contents
- front matter
- 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
- Appendix. Installation instructions
- index
- inside back cover