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.
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 100 pp. Englisch. N° de réf. du vendeur 9786208445911
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Paperback. Etat : new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9786208445911
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch. N° de réf. du vendeur 9786208445911
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Taschenbuch. Etat : Neu. Time Series Forecasting of Meteorological Parameters | A Refined Sliding Window Approach to Time Series Forecasting in Climate and Weather Data | Garima Jain (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208445911 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. N° de réf. du vendeur 133559478
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. N° de réf. du vendeur 9786208445911
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