Isbn: 9786208445911 - time series forecasting of meteorological parameters: a refined sliding window approach to time series forecasting in climate and weather data (10 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Taschenbuch. Condición: 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.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 100 pp. Englisch.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Taschenbuch. Condición: 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. …

Condición: New. PRINT ON DEMAND.

Taschenbuch. Condición: 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. …