Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting

arXiv:2403.00796v1 Announce Type: cross
Abstract: In this paper, we explore the application of Gaussian Processes (GPs) for predicting mean-reverting time series with an underlying structure, using relatively unexplored functional and augmented data structures. While many conventional forecasting methods concentrate on the short-term dynamics of time series data, GPs offer the potential to forecast not just the average prediction but the entire probability distribution over a future trajectory. This is particularly beneficial in financial contexts, where accurate predictions alone may not …

abstract application arxiv augmented data cs.ai cs.lg data dynamics explore financial financial forecasting forecasting functional gaussian processes gps mean paper prediction processes q-fin.st series stat.ml time series type