Traditional Prediction Techniques and Machine Learning Approaches for Financial Time Series Analysis

Accurate financial time series forecasting is critical for effective decision making in areas such as risk management, portfolio optimization, and trading. Given the complexity and volatility of financial markets, traditional forecasting methods often fail to capture the underlying dynamics. Recent advances in artificial neural network (ANN) forecasting research indicate that ANNs present a valuable alternative to traditional linear methods, such as autoregressive integrated moving average (ARIMA). However, time series are typically influenced by a combination of factors which require to consider both linear and non-linear characteristics. This paper proposes a new hybrid model that integrates ARIMA and ANN models such as long short-term memory and gated recurrent unit neural network to leverage the distinct strengths of both linear and non-linear modeling. Moreover, the goodness of the proposed model is evaluated through a comparative analysis of the ARIMA, ANN and Zhang hybrid model, using three financial datasets (i.e., Unicredit SpA stock price, EUR/USD exchange rate and Bitcoin closing price). Various absolute and relative error metrics, computed to evaluate the performance of models, can support the use of the proposed approach. The Diebold-Mariano (DM) test is also implemented to asses the significance of the obtained differences of the hybrid model with respect to the other competing models.