%0 Journal Article
%T Adaptive Learning in Short Time Series
%A Georgios Prokopos
%A Foteini Kyriazi
%J Theoretical Economics Letters
%P 674-688
%@ 2162-2086
%D 2025
%I Scientific Research Publishing
%R 10.4236/tel.2025.153036
%X 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.
%K Price Forecasting
%K Adaptive Learning
%K Quantile Regression
%K Energy-Agriculture Nexus
%K Volatility
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=143207