One of the most important elements of today's decision-making world, in both the public and the private sectors, is the forecasting of macroeconomic and financial variables. This applies to many industries including finance, education, and health care to name just a few. However, not many business analysts or developers people know how to use machine learning approach and technologies to build successful forecast applications. This book provides a practical introductory guide to time series forecasting with machine learning and Python for those hands-on readers.

Readers new to time series forecasting will be able to understand and deal better with:

• Time series forecasting concepts, such as horizon, frequency trend and seasonality.
• Evaluation of the time series forecasting models performance and accuracy.
• Understanding when to use neural networks instead of traditional time series models in time series forecasting.

The book shows readers practical instances of how these time series forecasting models can be applied to a real-world scenario by providing examples and using many machine learning components available in open-source Python packages, such as Scikit-learn, Keras and Tensorflow. The reader will also use other Python tools such as Jupyter notebooks to interactively explore data, transform it, and then develop time series forecasting models.