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.
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 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.
