Abstract The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of topic and multitude ingredients involved therein, besides complexity turning theory into practical implementations, limit use learning paradigm, preventing its widespread adoption across different fields applications. This self-contained survey engages introduces readers to principles algorithms Learning for Neural Networks. It provides an introduction from accessible, practical-algorithmic...

Nowadays, with the availability of massive amount trade data collected, dynamics financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage rapid, subtle movement assets in High Frequency Trading (HFT), automatic algorithm analyze detect patterns price change based on transaction records must be available. The multichannel, time-series representation naturally suggests tensor-based learning algorithms. this work, we investigate...

Abstract Managing the prediction of metrics in high‐frequency financial markets is a challenging task. An efficient way by monitoring dynamics limit order book to identify information edge. This paper describes first publicly available benchmark dataset for mid‐price prediction. We extracted normalized data representations time series five stocks from Nasdaq Nordic stock market period 10 consecutive days, leading ∼4,000,000 samples total. A day‐based anchored cross‐validation experimental...

Abstract The prediction of financial markets is a challenging yet important task. In modern electronically driven markets, traditional time‐series econometric methods often appear incapable capturing the true complexity multilevel interactions driving price dynamics. While recent research has established effectiveness machine learning (ML) models in applications, their intrinsic inability to deal with uncertainties, which great concern econometrics and real business constitutes major...

Financial time-series forecasting is one of the most challenging domains in field analysis. This mostly due to highly non-stationary and noisy nature financial data. With progressive efforts community design specialized neural networks incorporating prior domain knowledge, many analysis problems have been successfully tackled. The temporal attention mechanism a layer that recently gained popular-ity its ability focus on important events. In this paper, we propose based ideas multi-head...

Abstract Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces new method predicting future state synchronization two financial To this end, we use cross recurrence plot as nonlinear quantifying multidimensional coupling in domain series and determining their synchronization. We adopt deep learning framework methodologically addressing prediction based on features extracted from dynamically sub-sampled plots. provide...

Variational Inference (VI) is a commonly used technique for approximate Bayesian inference and uncertainty estimation in deep learning models, yet it comes at computational cost, as doubles the number of trainable parameters to represent uncertainty. This rapidly becomes challenging high-dimensional settings motivates use alternative techniques inference, such Monte Carlo Dropout (MCD) or Spectral-normalized Neural Gaussian Process (SNGP). However, methods have seen little adoption survival...

Variational Inference (VI) is a commonly used technique for approximate Bayesian inference and uncertainty estimation in deep learning models, yet it comes at computational cost, as doubles the number of trainable parameters to represent uncertainty. This rapidly becomes challenging high-dimensional settings motivates use alternative techniques inference, such Monte Carlo Dropout (MCD) or Spectral-normalized Neural Gaussian Process (SNGP). However, methods have seen little adoption survival...

We propose an optimization algorithm for variational inference (VI) in complex models. Our approach relies on natural gradient updates where the space is a Riemann manifold. develop efficient gaussian whose satisfy positive definite constraint covariance matrix. manifold Bayes precision matrix (MGVBP) solution provides simple update rules, straightforward to implement, and use of parameterization has significant computational advantage. Due its black-box nature, MGVBP stands as ready-to-use...

Bayesian methods can express uncertainty about their predictions, but have seen little adaptation in survival analysis using neural networks. Proper estimation is important high-risk domains, such as the healthcare or medical field, if machine learning are to be adopted for decision-making purposes, however, a known shortcoming of In this paper, we introduce use inference techniques networks that rely on Cox proportional hazard assumption, which discuss new flexible and effective...

The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling. Variational Inference is gaining popularity and attention as a robust approach for inference in complex machine learning models; however, its adoption econometrics finance limited. This paper discusses the extent to which constitutes reliable feasible alternative sampling GARCH-like models. Through large-scale experiment involving constituents S& P 500 index, several optimizers, variety...

We develop an optimization algorithm suitable for Bayesian learning in complex models. Our approach relies on natural gradient updates within a general black-box framework efficient training with limited model-specific derivations. It applies the class of exponential-family variational posterior distributions, which we extensively discuss Gaussian case have rather simple form. Quasi Black-box Variational Inference (QBVI) is readily applicable to wide inference problems and implementation as...

Several methods have been developed for the simulation of Hawkes process. The oldest approach is inverse sampling transform (ITS) suggested in \citep{ozaki1979maximum}, but rapidly abandoned favor more efficient alternatives. This manuscript shows that ITS can be conveniently discussed terms Lambert-W functions. An optimized and implementation suggests this computationally performing than recent alternatives available

The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling. Variational Inference is gaining popularity and attention as a robust approach for inference in complex machine learning models; however, its adoption econometrics finance limited. This paper discusses the extent to which constitutes reliable feasible alternative sampling GARCH-like models. Through large-scale experiment involving constituents S&P 500 index, several optimizers, variety...

Long-range correlation in financial time series reflects the complex dynamics of stock markets driven by algorithms and human decisions. Our analysis exploits ultrahigh frequency order book data from NASDAQ Nordic over a period three years to numerically estimate power-law scaling exponents using detrended fluctuation (DFA). We address inter-event durations (order order, trade trade, cancel cancel) as well cross-event (time submission its or cancel). find strong evidence long-range...