An Adversarial Learning Approach to Irregular Time-Series Forecasting
Keywords: Adversarial learning, Time-series forecasting, irregular time-series
TL;DR: We suggest that 1) existing outperforming methods often produce results that are overly unrealistic, 2) adversarial learning can effectively address this issue, particularly in the underexplored area of irregular time-series.
Abstract: Forecasting irregular time series presents significant challenges due to two key issues: the vulnerability of models to mean regression, driven by the noisy and complex nature of the data, and the limitations of traditional error-based evaluation metrics, which fail to capture meaningful patterns and penalize unrealistic forecasts. These problems result in forecasts that often misalign with human intuition. To tackle these challenges, we propose an adversarial learning framework with a deep analysis of adversarial components. Specifically, we emphasize the importance of balancing the modeling of global distribution (overall patterns) and transition dynamics (localized temporal changes) to better capture the nuances of irregular time series. Overall, this research provides practical insights for improving models and evaluation metrics, and pioneers the application of adversarial learning in the domian of irregular time-series forecasting.
Submission Number: 46
Loading