Work

Comparison of Feature Selection Methods for Wheat Price Forecasting

Year: 2026

Type: preprint

Abstract: A systematic comparison of 15+ feature selection methods and a baseline Lasso regression for weekly forecasting of CME Wheat futures (ZW). Rolling-window validation demonstrated that Spearman correlation provides the best balance of accuracy and stability: achieving a MAPE of 2.49%, Directional Accuracy of 59.5%, and a Composite Score of 0.79 — outperforming both simple statistical filters and complex ML wrappers (XGBoost, SHAP). This study provides practical, data-driven recommendations for quantitative researchers on selecting a method based on priorities: accuracy, stability, or computational speed.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22228430

Author: Podmogaev, Yuriy

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Zero hunger

Open Access status: green

OpenAlex ID: W7204929737

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