Modern Time Series Forecasting with Python

Modern Time Series Forecasting with Python is a comprehensive guide to mastering time series forecasting by leveraging the power of Python, machine learning, and deep learning. In this book, you will explore techniques like ARIMA to cutting-edge methods such as transformers, and gain practical experience applying these to real-world scenarios like energy forecasting. Empower yourself to create robust, industry-ready forecasting solutions.

What this Book will help me do

• Develop a strong foundation in time series data manipulation and visualization techniques.
• Understand and implement traditional methods, such as ARIMA, and advanced forecasting models.
• Learn to engineer features and apply ensembling techniques to enhance forecasting accuracy.
• Gain proficiency in building machine learning and deep learning architectures for time series forecasting.
• Master advanced techniques such as multi-step forecasting and cross-validation strategies.

Author(s)

Manu Joseph, the author of this book, is an experienced data scientist with extensive expertise in machine learning and time series forecasting. He possesses a keen ability to translate complex technical concepts into precise, actionable guidance. Manu's practical approach and focus on real-world applications make his works highly valuable for professionals and learners alike.

Who is it for?

This book is ideally suited for machine learning engineers, Python developers, and data scientists interested in time series analysis. If you have basic Python knowledge and curiosity about forecasting, this resource will help you develop practical, industry-ready skills. Experienced practitioners in time series forecasting will find advanced techniques and modern approaches, like deep learning, covered in great detail for furthering their expertise.