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

In this book 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

This accessible book teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. You’ll learn statistical and deep learning methods for time series forecasting, fully demonstrated with 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.

About the listener

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.

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.