Time Series Forecasting of Meteorological Parameters

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.