Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models

TL;DR

This paper introduces a Time Series Transformer model for predicting bank stability indices, demonstrating superior accuracy over traditional models by capturing complex temporal dependencies in financial data.

Contribution

The paper presents a novel application of the Time Series Transformer for bank stability prediction, outperforming existing models in accuracy and handling multidimensional data effectively.

Findings

Time Series Transformer outperforms LSTM, GRU, CNN, TCN, and RNN-Transformer in MSE and MAE.

The model effectively captures complex temporal dependencies in financial data.

Provides a new approach for financial risk management using advanced deep learning techniques.

Abstract

This paper aims to study the prediction of the bank stability index based on the Time Series Transformer model. The bank stability index is an important indicator to measure the health status and risk resistance of financial institutions. Traditional prediction methods are difficult to adapt to complex market changes because they rely on single-dimensional macroeconomic data. This paper proposes a prediction framework based on the Time Series Transformer, which uses the self-attention mechanism of the model to capture the complex temporal dependencies and nonlinear relationships in financial data. Through experiments, we compare the model with LSTM, GRU, CNN, TCN and RNN-Transformer models. The experimental results show that the Time Series Transformer model outperforms other models in both mean square error (MSE) and mean absolute error (MAE) evaluation indicators, showing strong prediction ability. This shows that the Time Series Transformer model can better handle multidimensional time series data in bank stability prediction, providing new technical approaches and solutions for financial risk management.