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Chou, J., & Tran, D. (2018). Forecasting Energy Consumption Time Series Using Machine Learning Techniques Based on Usage Patterns of Residential Householders. Energy, 165, 709-726.
https://doi.org/10.1016/j.energy.2018.09.144

has been cited by the following article:

• TITLE: Adaptive Learning in Short Time Series

  AUTHORS: Georgios Prokopos, Foteini Kyriazi

  KEYWORDS: Price Forecasting, Adaptive Learning, Quantile Regression, Energy-Agriculture Nexus, Volatility

  JOURNAL NAME: Theoretical Economics Letters, Vol.15 No.3, June 12, 2025

  ABSTRACT: This paper applies the novel adaptive learning methodology to forecast agricultural and energy prices in Greece’s volatile, data-scarce markets. We combine traditional ordinary least squares with quantile regression techniques within this framework, achieving up to 27% lower forecast errors compared to conventional benchmarks. Our analysis reveals distinct performance patterns: quantile regression demonstrates superior accuracy for volatile commodities (e.g., barley), while ordinary least squares performs better for stable markets (e.g., maize). The learning rate parameter γ proves crucial in adapting to market conditions. These findings provide policymakers with an enhanced tool for analyzing energy-agriculture price linkages and managing market volatility, particularly in small, open economies facing data limitations.