Название: Ultimate Enterprise Data Analysis and Forecasting using Python: Leverage Cloud platforms with Azure Time Series Insights and AWS Forecast Components for Time Series Analysis and Forecasting with Deep learning Modeling using PythonАвтор: Shanthababu Pandian
Издательство: Orange Education Pvt Ltd, AVA
Год: December 2023
Страниц: 503
Язык: английский
Формат: epub (true)
Размер: 18.3 MB
Practical Approaches to Time Series Analysis and Forecasting using Python for Informed Decision-Making.
Book Description:
This book covers various aspects of Time Series Analysis and Forecasting using the Python language, emphasizing the importance of time series analysis from an industry perspective for in-depth analysis and forecasting, with real-time use cases and required examples. The primary objective of this book is to provide a detailed pack of time series analysis and forecasting methods, essential in the current digital market, and grow business opportunities using various techniques from an AIML perspective. This book aims to connect the Time Series and Forecasting problem statements across multiple industries and demonstrate how to provide solutions using currently available tools, technology, and evidence of success stories. This book promises that by the end of the reading, the readers will understand time series and forecasting techniques, and also learn how to analyze, design, and maintain the solutions. In this manner, readers can follow the correct path to take the time series components, work on them with Python packages, and understand the data for analysis and productive solutions, such as predicting or forecasting.
This book covers the expectations of Data Analysts, Data Scientists, and Machine Learning Engineers who will be involved in time series analysis and forecasting-related projects. This book helps those interested in time series analysis. The book begins with an introduction to Python and its essential packages. It then delves into various aspects of time series data analysis and models from both traditional and ML methods, followed by their implementation in the cloud environment.
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.
This book is divided into four parts and 11 chapters. It will cover everything, starting with an introduction to Python and its essential packages for data science and machine learning. The book will explore various time series analysis and forecasting techniques, along with the implementation of time series analysis in Azure and AWS environments.
Contents:
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