Time Series Forecasting of Meteorological Parameters - Jain, Garima;Jain, Ankush;Jaiswal, Anuj
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This book focuses on the time series forecasting of critical meteorological parameters including temperature, rainfall, humidity, and wind. It explores classical statistical models such as ARIMA, Holt-Winters, and Exponential Smoothing, along with a novel enhancement-the Modified Sliding Window Algorithm. The objective is to improve prediction accuracy in meteorological datasets by applying adaptive techniques. Real-time weather data has been analyzed using these models, and a comparative study highlights the performance of each. This work is beneficial for researchers, meteorologists, and…mehr

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Produktbeschreibung
This book focuses on the time series forecasting of critical meteorological parameters including temperature, rainfall, humidity, and wind. It explores classical statistical models such as ARIMA, Holt-Winters, and Exponential Smoothing, along with a novel enhancement-the Modified Sliding Window Algorithm. The objective is to improve prediction accuracy in meteorological datasets by applying adaptive techniques. Real-time weather data has been analyzed using these models, and a comparative study highlights the performance of each. This work is beneficial for researchers, meteorologists, and data scientists working in climate modeling and weather prediction.
Autorenporträt
Garima Jain is an academic and researcher with expertise in data science, time series analysis, and environmental modeling. She serves as Deputy Head of the CSBS Department at Noida Institute of Engineering and Technology, Greater Noida, and has published widely in the fields of statistical modeling, AI, and interdisciplinary applications.