Time Series Forecasting in Python

Time Series Forecasting in Python is a book that teaches how to build predictive models from time-based patterns in data, using both traditional statistical and new deep learning approaches. It covers univariate, multivariate, and deep learning forecasting models, and provides hands-on projects for forecasting air travel, drug prescriptions, and Johnson & Johnson earnings.

Format: Paperback / softback
Length: 456 pages
Publication date: 04 October 2022
Publisher: Manning Publications

In this comprehensive Python course, you will delve into the realm of time series forecasting, where you will learn to create powerful predictive models from time-based data. Every model you develop will be relevant, practical, and easily implemented using Python. You will explore fascinating real-world datasets, such as Google's daily stock price and economic data for the United States, and progress from the fundamentals to developing large-scale models that leverage deep learning tools like TensorFlow.

Time Series Forecasting in Python will equip you with the skills to apply time series forecasting and obtain immediate, meaningful predictions. You will learn both traditional statistical and cutting-edge deep learning models for time series forecasting, all thoroughly illustrated with Python source code.

By the end of this course, you will have a solid foundation in time series forecasting and be ready to apply your knowledge to real-world scenarios. Whether you are a data analyst, scientist, or engineer, this course will enhance your ability to make informed decisions based on historical data. So, join us and embark on this exciting journey of time series forecasting in Python!

Weight: 802g
Dimension: 187 x 235 x 36 (mm)
ISBN-13: 9781617299889