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

KEY FEATURES 

● Comprehensive Resource for Python-Based Time Series Analysis and Forecasting. 
● Delve into real-world applications with industry-specific 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 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. 

WHAT WILL YOU LEARN 

● Explore Time Series Data Analysis and Forecasting, covering components and significance. 
● Gain a practical understanding through hands-on examples and real-world 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 data-related 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.

1. Introduction to Python and its key packages for DS and ML Projects
2. Python for Time Series Data Analysis
3. Time Series Analysis and its Components
4. Time Series Analysis and Forecasting Opportunities in Various Industries
5. Exploring various aspects of Time Series Analysis and Forecasting
6. Exploring Time Series Models - AR, MA, ARMA, and ARIMA
7. Understanding Exponential Smoothing and ETS Methods in TSA
8. Exploring Vector Autoregression and its Subsets (VAR, VMA, and VARMA)
9. Deep Learning for Time Series Analysis and Forecasting
10. Azure Time Series Insights
11. AWSForecast
       Index

Shanthababu Pandian holds a Bachelor’s degree in engineering in Electronics and Communication, followed by three Master's degrees - MTech, MBA, and M.S. - from a prestigious university in India. Additionally, he completed a Post Graduate Program in Artificial Intelligence and Machine Learning from the University of Texas, along with a Post Graduate Certification in Data Science from the Indian Institute of Technology, Guwahati.

With over 21 years of extensive experience in information technology (IT), Shanthababu specializes in data engineering and analytics solutions, development and implementation using agile methodologies, and building complex data models for Business Intelligence (BI) and data science products for various customers located across the UK and US regions.

As part of his responsibilities, Shanthababu is accustomed to liaising with key stakeholders and business teams, gathering and eliciting requirements, and architecting cost-effective data modeling solutions as per delivery frameworks. He is efficient at mitigating project delivery risks while managing stakeholders and leading project team members across different locations. 

• Technical Author in Analytics Vidhya, Data Science Central and Shantha’s AI view in Data Platform/ Data Architecture /Data Science/Machine Learning/AI around 50+
• Analytics Vidhya
• https://www.analyticsvidhya.com/blog/author/shanthababu/
• Data Science Central
• https://www.datasciencecentral.com/author/shanthababup/
• Shantha’s AI Views
• https://shanthababu.com/blog-2/
• Technical author engagement with the following Data Industry
• Octopus Data Inc. Shenzhen, Guangdong, China
• dezyre.com (https://www.projectpro.io/)
• neptune.ai
• StrictlyByTheNumbers.com

• Technical Book Reviewer for Pack Publication – BIRMINGHAM, UK, Reviewing more than 30 books from international authors on various technical aspects.
• National and International Technical Speaker in the AIML domain, with more than 40 webinars presented.
• Technical Mentor for various Engineering and Technical institutions across Tamil Nadu, India 

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ABOUT TECHNICAL REVIEWERS

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S.Ponmalar is an Academician with over 25 years of field and domain experience in Information and Communication Engineering including optical communication, Networks, Machine Learning, and Computer vision. She has obtained her Doctoral degree in Information and Communcation Engineering from Anna University. She is a recognized supervisor and guides many PG and PhD scholars. She has published over 30 research papers in many reputed Journals and Conferences. Her interest in upgrading with recent trends and technology has obligated her to read and review articles and books from different publishers.

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Vidhya Veerabahu is a Professor and Head of IT at Sri Venkateswara College of Engineering, Sriperumbudur. With over 23 years of experience in the fields of data science and Natural Language Processing (NLP), she is an expert in these domains. She has used statistical, semantical, and contextual methods to automate various specific processes in the education sector. Additionally, she has developed several smart mobile-based applications to provide solutions to societal challenges and has helped the organization analyze trends for better decision-making. She has published and presented over 40 papers in international journals and conferences.