Time Series Forecasting Techniques for Climate Trend Prediction

Abstract

Climate change is a pressing global issue that profoundly impacts ecosystems, economies, and societies. Accurate climate trend prediction is crucial for informed decision-making and mitigation strategies. This study focuses on time series forecasting techniques as vital tools in predicting climate trends. It explores the complexities of climate time series data and the challenges associated with the data. The study explores traditional methods like Autoregressive Integrated Moving Average (ARIMA), highlighting their applicability and limitations. It also showcases the power of machine learning and statistical techniques in addressing climate data intricacies through real-world examples. In the era of technology, deep learning (DL) approaches, including recurrent neural networks, Long short-term memory (LSTM), Gated Recurrent Unit (GRU), and transformer-based models, are emerging for climate change forecasting. The study looks ahead to ongoing research and trends in climate time series forecasting, outlining challenges and promising areas for exploration.