Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins

Abstract

This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre-, during, and post-pandemic periods. Daily financial market indices and price observations are used to assess the forecasting models. We compare statistical, machine learning, and deep learning forecasting models to evaluate the financial markets, such as the ARIMA, hybrid ETS-ANN, and kNN predictive models. The study results indicate that predicting financial market fluctuations is challenging, and the accuracy levels are generally low in several instances. ARIMA and hybrid ETS-ANN models perform better over extended periods compared to the kNN model, with ARIMA being the best-performing model in 2018–2021 and the hybrid ETS-ANN model being the best-performing model in most of the other subperiods. Still, the kNN model outperforms the others in several periods, depending on the observed accuracy measure. Researchers have advocated using parametric and non-parametric modeling combinations to generate better results. In this study, the results suggest that the hybrid ETS-ANN model is the best-performing model despite its moderate level of accuracy. Thus, the hybrid ETS-ANN model is a promising financial time series forecasting approach. The findings offer financial analysts an additional source that can provide valuable insights for investment decisions.

Method for the Statistical Analysis of the Signals Generated by an Acquisition Card for Pulse Measurement

“…A quantification of performance metrics is presented in [18]. These parameters have been used separately in the comparison of scenarios of multiple areas and purposes, some of which can be seen in [43][44][45][46][47][48].…”

“…Additionally, this research addresses some of the most commonly used metrics in measuring the error between two series [41][42][43][44][45][46]. The nomenclature used in this document can be seen in the index presented in Abbreviations.…”

Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention

“…In recent decades, many researchers and practitioners have tried to predict stock prices using various methods, including time-series-based prediction methods [7,8], machine learningbased prediction methods [9,10], deep learning-based prediction methods [11][12][13][14], and so on. However, due to the characteristics of stock prices, such as non-linearity, high noise, and variability, it is often difficult to achieve the desired prediction results with these methods [15][16][17][18][19][20][21].…”