Bayesian Model Selection for Addressing Cold-Start Problems in Partitioned Time Series Prediction

Abstract

How to effectively predict outcomes when initial time series data are limited remains unclear. This study investigated the efficiency of Bayesian model selection to address the lack of initial data for time series analysis, particularly in cold-start scenarios—a common challenge in predictive modeling. We utilized a comprehensive approach that juxtaposed observational data against various candidate models through strategic partitioning. This method contrasted traditional reliance on distance measures like the L2 norm. Instead, it applied statistical tests to validate model efficacy. Notably, the introduction of an interactive visualization tool featuring a slide bar for setting significance levels marked a significant advancement over conventional p-value displays. Our results affirm that when observational data align with a candidate model, effective predictions are possible, albeit with necessary considerations of stationarity and potential structural breaks. These findings underscore the potential of Bayesian methods in predictive analytics, especially when initial data are scarce or incomplete. This research not only enhances our understanding of model selection dynamics but also sets the stage for future investigations into more refined predictive frameworks.