Análisis de anomalías climáticas en series temporales de precipitación en el Austro ecuatoriano utilizando fuentes textuales

Abstract

The increase in climate variability and the growing frequency of extreme events represent significant challenges for society and the environment. Gaining a deep understanding of past anomalous events is essential for planning, adaptation, and mitigation of the impact of similar events in the future. Climate time series contain valuable information about these patterns, but analyzing and interpreting significant deviations requires advanced analytical tools. Although established methods exist for anomaly detection in time series, they often lack enriched contextual interpretation. The absence of this contextualization limits the practical value of anomaly detection for informed decision-making and anticipating future scenarios. This work proposes a process that integrates anomaly detection techniques in time series with the incorporation of contextual information from textual sources. Statistical approaches, machine learning, deep learning, and large language models are used to identify significant deviations in historical precipitation data. The detected anomalies are complemented with relevant information extracted from news sources through a manual and semi-automated process, and are stored in a structured relational database. Additionally, a numerical and visual similarity analysis is implemented to compare anomalies with one another, enabling the identification of recurring patterns over time and providing a more comprehensive un derstanding of extreme climate events.