Variance Stabilizing Transformations for Electricity Spot Price Forecasting

TL;DR: In this article, the authors evaluate 16 variance stabilizing transformations within a comprehensive forecasting study involving two model classes (regression models, neural networks) and 12 datasets from diverse power markets, and show that the probability integral transform combined with the standard Gaussian distribution yields the best approach.

Abstract: Most electricity spot price series exhibit price spikes. These extreme observations may significantly impact the obtained model estimates and hence reduce efficiency of the employed predictive algorithms. For markets with only positive prices, the logarithmic transform is the single most commonly used technique to reduce spike severity and consequently stabilize the variance. However, for datasets with very close to zero (like the Spanish) or negative (like the German) prices the log-transform is not feasible. What reasonable choices do we have then? To address this issue, we evaluate 16 variance stabilizing transformations within a comprehensive forecasting study involving two model classes (regression models, neural networks) and 12 datasets from diverse power markets. We show that the probability integral transform combined with the standard Gaussian distribution yields the best approach, significantly better than many of the considered alternatives.