Practical Approaches to Time Series Analysis and Forecasting using Python for Informed Decision-Making

Book DescriptionEmbark 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 real-world applications across various industries through compelling use-case 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 cutting-edge AWS Forecast components, unlocking the cloud's power for advanced and scalable time series forecasting.

Table of Contents1. Introduction to Python and its key packages for DS and ML Projects2. Python for Time Series Data Analysis3. Time Series Analysis and its Components4. Time Series Analysis and Forecasting Opportunities in Various Industries5. Exploring various aspects of Time Series Analysis and Forecasting6. Exploring Time Series Models - AR, MA, ARMA, and ARIMA7. Understanding Exponential Smoothing and ETS Methods in TSA8. Exploring Vector Autoregression and its Subsets (VAR, VMA, and VARMA)9. Deep Learning for Time Series Analysis and Forecasting10. Azure Time Series Insights11. AWSForecast Index