Time Series Forecasting Techniques for Climate Trend Prediction

Introduction

Climate change is one of the foremost challenges confronting our planet today, wielding profound impacts on ecosystems, economies, and societies worldwide (Athiyarath et al., 2020). Accurately predicting climate trends has become paramount for informed decision-making and effective mitigation strategies. Within this context, time series forecasting emerges as a crucial tool in the arsenal of climate researchers (Ensafi et al., 2022). Time series forecasting holds unique importance in climate research due to climate data's dynamic and evolving nature. The intricate patterns, seasonality, trends, and inherent noise within climate time series pose challenges that necessitate advanced analytical techniques (Cruz-Nájera et al., 2022). This chapter embarks on a comprehensive exploration of the role of time series forecasting in predicting climate trends.

This chapter will traverse traditional time series forecasting methods, commencing with established approaches like ARIMA and its variants (Singh et al., 2019). Acknowledging their strengths and limitations, we will then delve into the integration of machine learning and statistical techniques, showcasing their efficacy in addressing the peculiarities of climate data. In the contemporary technological landscape, the chapter will highlight the ascendancy of DL approaches featuring Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models.

Moreover, the exploration extends to transformer-based models, which, although initially prominent in natural language processing, exhibit promise in time series forecasting (Hidayatullah et al., 2023; Sherstinsky, 2020). Real-world examples and case studies will provide tangible insights into the practical applications of these diverse forecasting methodologies. As we progress, the chapter will reflect on the current research state and the future (Morales-García et al., 2023). Ongoing research and emerging trends in climate time series forecasting will be discussed, outlining the challenges that persist and the promising areas for further exploration. Through this comprehensive overview, the chapter seeks to contribute to the collective understanding of climate trends, providing valuable insights for researchers, practitioners, and policymakers alike (Muzalyova et al., 2021). Climate time series data, a fundamental component of climate research, comprises records of various climatic variables collected at regular intervals (Weekaew et al., 2021). This data is sourced from a diverse array of monitoring systems, including meteorological stations, satellites, and weather balloons, offering a comprehensive perspective on the Earth's climate (Mishra et al., 2020). The richness of climate time series data lies in its ability to capture essential characteristics such as seasonality, depicting cyclical patterns corresponding to different seasons; trends, illustrating long-term shifts in climate variables over extended periods; and noise, representing random and unpredictable variations that can result from measurement errors or external factors (Simmons et al., 2023).