Requirements: .ePUB reader, 18.3 MB
Overview: Practical Approaches to Time Series Analysis and Forecasting using Python for Informed Decision-Making. 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.
Genre: Non-Fiction > Tech & Devices
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