Book description
Build predictive models from timebased 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 timebased data. Every model you create is relevant, useful, and easy to implement with Python. You’ll explore interesting realworld datasets like Google’s daily stock price and economic data for the USA, quickly progressing from the basics to developing largescale 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 timecentric 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 timebased 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.
Quotes
The importance of time series analysis cannot be overstated. This book provides key techniques to deal with time series data in realworld 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
 6.1 Forecasting bandwidth usage for data centers
 6.2 Examining the autoregressive moving average process
 6.3 Identifying a stationary ARMA process
 6.4 Devising a general modeling procedure
 6.5 Applying the general modeling procedure
 6.6 Forecasting bandwidth usage
 6.7 Next steps
 6.8 Exercises
 Summary
 7 Forecasting nonstationary 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. Largescale 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
Product information
 Title: Time Series Forecasting in Python
 Author(s):
 Release date: October 2022
 Publisher(s): Manning Publications
 ISBN: 9781617299889
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