Forecasting Highly Volatile Time Series: An Approach Based on Encoder–Only Transformers

High-precision time-series forecasting allows companies to better allocate resources, improve their competitiveness, and increase revenues. In most real-world cases, however, time series are highly volatile and cannot be used for forecasting together with classical statistical methods, which usually yield errors of around 30% or even more. Thus, the goal of this work is to present an approach to obtaining day-ahead forecasts of electricity consumption based on such volatile time series, along with data preprocessing for volatility attenuation. For a thorough understanding, predictions were computed using various methods based on either Artificial Intelligence or purely statistical algorithms. The architectures based on the Transformer were optimized through Brute Force, while the N-BEATS architecture was optimized with Brute Force and OPTUNA because of the highly stochastic nature of the time series. The best method was based on an Encoder-only Transformer, which resulted in an approximate prediction error of 11.63%—far below the error of about 30% usually accepted in current practice. In addition, a procedure was developed to determine the maximum theoretical Pearson Correlation Coefficient between forecast and actual power demand.