Enhancing Time Series Forecasting and Uncertainty Estimation with Bayesian Neural Networks

Abstract

Forecasts of time series are quite important in economics and finance as well as in weather
predictions. Traditional models like ARIMA and SARIMA face a problem in handling
complex patterns; therefore, there is a need to design more complex models such as LSTM
networks and Transformers. While these models have been able to overcome quite a few
limitations, they still fall short when it comes to measuring uncertainty in prediction. This is
required in high stake applications like stock market forecasting. Bayesian Neural Networks
assist by bringing uncertainty directly into the model to offer probabilistic predictions that
enhance decision making. It is focused on the integration of BNNs into time series
forecasting in the stock market and compares their performance against a great combination
of traditional and modern machine learning models. Results of the study show that BNNs
boost accuracy in predictions and add richness and robustness to the ability to explain its
prediction. This is a particularly useful property of BNNs when the tasks involve dealing with
uncertainty. BNNs enable one to make informed decisions in uncertain environments, such as
the stock market. It thereby provides a detailed perspective on how much reliance may be
placed on the prediction. The focus of the study is to create a novel hybrid methodology
combining BNN and custom Transformer-based Deep Learning model, taking advantage of
the strongest capabilities each technique has to offer. The practical outcome of using BNNs
in financial prediction will also be discussed, which include and are not limited to improving
risk management tactics and methods of portfolio optimization.