Tabular Data Models for Predicting Art Auction Results
Year: 2024
Type: article
Abstract: Predicting art auction results presents a unique challenge due to the complexity and variability of factors influencing artwork prices. This study explores a range of machine learning architectures designed to forecast auction outcomes using tabular data, including historical auction records, artwork characteristics, artist profiles, and market indicators. We evaluate traditional models such as LinearModel, K-Nearest Neighbors, DecisionTree, RandomForest, XGBoost, CatBoost, LightGBM, MLP, VIME, ModelTree, DeepGBM, DeepFM, and SAINT. By comparing the performance of these models on a dataset comprising extensive auction results, we provide insights into their relative effectiveness across different scenarios. Additionally, we address the interpretability of models, which is crucial for understanding the influence of various features on predictions. The results suggest that while some models perform better than others, no single approach offers consistently high accuracy across all cases. This study provides guidance for auction houses, art investors, and market analysts in refining predictive approaches, identifying key challenges, and understanding where further improvements are needed for more accurate data-driven decisions in the art market.
Source: Applied Sciences
DOI: 10.3390/app142311006
Authors: Patryk Mauer, Szczepan Paszkiel
Institution: Opole University of Technology
Language: English
Subfield: Visual Arts and Performing Arts
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4404765637