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The development of early warning systems for systemic crises has recently received growing interests. Recent studies have proposed possible solutions to address this challenging topic, in particular by means of cutting-edge artificial intelligence (AI) approaches. Financial data are fundamentally characterized by intrinsic temporal dynamics and the presence of both short-/long-term interactions. Hence, it is of paramount importance, when validating the proposed solutions to adopt validation strategies which consider this aspect. To this aim, we show here how Temporal Cross Validation (TCV) deeply affects the models’ learning. Moreover, to take into account the data imbalance often characterizing these models, we combine the TCV with a popular solution, which is the SMOTE (Synthetic Minority Oversampling TEchnique) algorithm.

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Time Sensitive and Oversampling Learning for Systemic Crisis Forecasting

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Francesco De Nicolò,
  • Marianna La Rocca,
  • Antonio Marrone,
  • Alfonso Monaco,
  • Sabina Tangaro,
  • Nicola Amoroso,
  • Roberto Bellotti

摘要

The development of early warning systems for systemic crises has recently received growing interests. Recent studies have proposed possible solutions to address this challenging topic, in particular by means of cutting-edge artificial intelligence (AI) approaches. Financial data are fundamentally characterized by intrinsic temporal dynamics and the presence of both short-/long-term interactions. Hence, it is of paramount importance, when validating the proposed solutions to adopt validation strategies which consider this aspect. To this aim, we show here how Temporal Cross Validation (TCV) deeply affects the models’ learning. Moreover, to take into account the data imbalance often characterizing these models, we combine the TCV with a popular solution, which is the SMOTE (Synthetic Minority Oversampling TEchnique) algorithm.