Variance Stabilizing Transformations for Electricity Price Forecasting in Periods of Increased Volatility

TL;DR

This paper demonstrates that variance stabilizing transformations, especially a new parametrized asinh, significantly improve electricity price forecasts during volatile periods, with reductions in forecast errors up to 17.7%.

Contribution

It introduces a novel parametrization of the asinh transformation and systematically analyzes its effectiveness for improving forecasts under high volatility.

Findings

VSTs reduce forecast errors by up to 14.6% for LEAR and 8.7% for NARX models.

The new parametrized asinh outperforms the standard form consistently.

Rolling averaging across transformations yields the most robust error reductions, up to 17.7%.

Abstract

Accurate day-ahead electricity price forecasts are critical for power system operation and market participation, yet growing renewable penetration and recent crises have caused unprecedented volatility that challenges standard models. This paper revisits variance stabilizing transformations (VSTs) as a preprocessing tool by introducing a novel parametrization of the asinh transformation, systematically analyzing parameter sensitivity and calibration window size, and explicitly testing performance under volatile market regimes. Using data from Germany, Spain, and France over 2015-2024 with two model classes (NARX and LEAR), we show that VSTs substantially reduce forecast errors, with gains of up to 14.6% for LEAR and 8.7% for NARX relative to untransformed benchmarks. The new parametrized asinh consistently outperforms its standard form, while rolling averaging across transformations delivers the most robust improvements, reducing errors by up to 17.7%. Results demonstrate that VSTs are especially valuable in volatile regimes, making them a powerful tool for enhancing electricity price forecasting in today's power markets.