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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1852
Title: Time series data analytics leveraging machine learning algorithms_ a scenario in meteorology
Authors: Ha Cam Tu 
Keywords: Time series data;Machine learning algorithms;Meteorology
Issue Date: 2024
Abstract: 
Meteorological forecasting is vital for agricultural planning and development, especially in Vietnam, where agriculture plays an important role in the national economy. This thesis explores the application of machine learning algorithms to time series data analytics for predicting air temperature which is the target variable. The study applies and compares various models, including ARIMA, SARIMAX, XGBoost, and Prophet. These models are evaluated on their ability to predict weather precisely, using previous data collected from 2014 to 2024 from Vinh Long - an agricultural western province of Vietnam. The findings show that SARIMAX, with its function to handle seasonality and exogenous variables, ensures the highest accuracy at the same time, although it requires a significant computing and execution time. Moreover, XGBoost and Prophet also demonstrate strong performance, particularly when dealing with large datasets and data with seasonal patterns. The thesis contributes to the related field by providing a comprehensive analysis and comparison between these models, offering practical insights and new technique combinations that can be utilized for future meteorological forecasting research. The research states the potential of machine learning in enhancing agricultural productivity through precise weather predictions, discussing limitations and challenges in scalability, and real-time data integration.
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/1852
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Computer Science (CS)

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