Augmented bilinear network for incremental multi-stock time-series classification

Predicting the State of Synchronization of Financial Time Series Using Cross Recurrence Plots

“…By considering the stock market as a complex system, it is natural to apply such methods for addressing those prediction problems where the application setting and assumptions beneath standard econometric techniques are stringent or inadequate. Indeed, it has extensively been shown that, in financial applications, deep learning (DL) models are often capable of outperforming traditional approaches due to their ability to learn complex data representations based on end-to-end data-driven training, see, e.g., [19][20][21][22][23][24]. DL models have been adopted for a variety of problems ranging from price prediction [25][26][27][28][29], limitorder book-based mid-price prediction [20,21,23,30,31], and volatility prediction [32][33][34].…”

“…Indeed, it has extensively been shown that, in financial applications, deep learning (DL) models are often capable of outperforming traditional approaches due to their ability to learn complex data representations based on end-to-end data-driven training, see, e.g., [19][20][21][22][23][24]. DL models have been adopted for a variety of problems ranging from price prediction [25][26][27][28][29], limitorder book-based mid-price prediction [20,21,23,30,31], and volatility prediction [32][33][34].…”