Description

Practical Approaches to Time Series Analysis and Forecasting using Python for Informed DecisionMaking

KEY FEATURES 

Comprehensive Resource for PythonBased Time Series Analysis and Forecasting. 
Delve into realworld applications with industryspecific case studies. 
Extract valuable insights by solving time series challenges across various sectors. 
Understand the significance of Azure Time Series Insights and AWS Forecast components. 
Practical insights into leveraging cloud platforms for efficient time series forecasting.

DESCRIPTION

Embark on a transformative journey through the intricacies of time series analysis and forecasting with this comprehensive handbook. Beginning with the essential packages for data science and machine learning projects you will delve into Python's prowess for efficient time series data analysis, exploring the core components and realworld applications across various industries through compelling usecase studies. From understanding classical models like AR, MA, ARMA, and ARIMA to exploring advanced techniques such as exponential smoothing and ETS methods, this guide ensures a deep understanding of the subject. 

It will help you navigate the complexities of vector autoregression (VAR, VMA, VARMA) and elevate your skills with a deep dive into deep learning techniques for time series analysis. By the end of this book, you will be able to harness the capabilities of Azure Time Series Insights and explore the cuttingedge AWS Forecast components, unlocking the cloud's power for advanced and scalable time series forecasting. 

WHAT WILL YOU LEARN 

Explore Time Series Data Analysis and Forecasting, covering components and significance. 
Gain a practical understanding through handson examples and realworld case studies. 
Master Time Series Models (AR, MA, ARMA, ARIMA, VAR, VMA, VARMA) with executable samples. 
Delve into Deep Learning for Time Series Analysis, demystified with classical examples. 
Actively engage with Azure Time Series Insights and AWS Forecast components for a contemporary perspective.

WHO IS THIS BOOK FOR?

This book caters to beginners, intermediates, and practitioners in datarelated fields such as Data Analysts, Data Scientists, and Machine Learning Engineers, as well as those venturing into Time Series Analysis and Forecasting. It assumes readers have a foundational understanding of programming languages (C, C++, Python), data structures, statistics, and visualization concepts. With a focus on specific projects, it also functions as a quick reference for advanced users.