Название: Overview of Practical Time Series Forecasting using Python: Forecast AirQuality using algorithms like SARIMAXАвтор: Aditya Kaushal
Издательство: Amazon.com Services LLC
Год: 2021
Язык: английский
Формат: pdf, azw3, epub
Размер: 14.6 MB
This is a short book to show the readers how to build a Time Series Model using mathematical models, Python and concepts of statistics to predict real-time air quality in a local mapped area by using open source data. The main objective of this book is to teach the readers about forecasting algorithms like SARIMAX and how to build a Python project to forecast and monitor air pollution to track personal exposure to PM 2.5. At the end of the book, you will have a good understanding of SARIMAX Algorithm to make a good forecast Particulate Matter 2.5 (PM 2.5) similar to what Sci-kit - Learn regression algorithms provide. This book can be a foundation to build a mobile application/web application to forecast air quality.
Prerequisites:
The readers are expected to have a basic understanding and hands-on-experience with Python Programming knowledge. You should be familiar with the concept of time series forecasting. The concept of Time Series forecasting requires you to have some basic or beginner level knowledge of statistics and mathematical concepts like Averages, and Moving Averages.
This book would straightaway deep dive into the implementation with the code snippets and all the visualizations. It is an requirement to be familiar with Python, Scikit Libraries (NumPy, Pandas, Seaborn, Matplotlib), and other miscellaneous libraries. Other than that, it is good to have a good understanding of mathematical concepts like Moving Averages and other statistical concepts.
The utilization of NumPy, Pandas, Matplotlib, Seaborn, Time Series Forecasting Algorithms like (SARIMAX) Statistical Components, Tableau and Python will help you to gain practical exposure to implement a full-fledged Flask web application to forecast air quality.
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