Increasing Time Series Forecasting Performance with Dynamic System Copula-Based Ensemble Models

• Ademir Neto UFCA
• Tiago Ferreira UFRPE
• Paulo Firmino UFCA

Resumo

Time series forecasting has significantly benefited from ensemble models, which consistently outperform individual approaches by combining predictions from diverse methodologies. This paper proposes a novel ensemble framework based on the copula formalism, enabling dynamic selection of copula functions tailored to the characteristics of each dataset. Our approach integrates three widely recognized forecasting models: ARIMA, Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) neural networks and applies them to five key financial time series: Petrobras, Google, Nasdaq, S&P500, and the GBT to USD exchange rate. The ensemble combines predictions using multiple copula functions and compares their performance against traditional ensemble methods, such as simple average, simple median, and MLP-based combination. Experimental results show that the proposed system achieves mean absolute percentage errors (MAPE) below 0.71, outperforming standard ensemble strategies. This improvement highlights the effectiveness of copula-based modeling as an adaptive and flexible tool for predictive fusion. We conclude that incorporating copula theory into ensemble learning brings substantial advancements in time series forecasting, as it allows dynamic model aggregation according to data behavior. The proposed method bridges statistical foundations and artificial intelligence, offering promising results in financial applications and potential for broader use across various forecasting domains.