Ultimate Enterprise Data Analysis and Forecasting using Python Leverage Cloud platforms with Azure Time Series Insights and AW

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Free Download Ultimate Enterprise Data Analysis and Forecasting Using Python
by Shanthababu Pandian;
English | 2024 | ISBN: 8119416449 | 444 pages | True PDF | 81.81 MB

Practical Approaches to Time Series Analysis and Forecasting using Python for Informed Decision-Making
Book 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.
Table of Contents
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



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