Subdivided Clustering for Enhanced Predictive Accuracy

Jeongwoo Kim ()
Additional contact information
Jeongwoo Kim: Gangneung Wonju National University

Computational Economics, 2025, vol. 66, issue 4, No 23, 3436 pages

Abstract: Abstract In the regression-based prediction problem using clustering methods, it can be questionable to follow the conventional clustering method that considers every observation in data to obtain well-partitioned clusters. Unlike the classification problem, when aiming to predict a target value from a temporal perspective, it might not be efficient to focus on partitioning the entire data. However, it could be more efficient to focus on finding a single cluster right next to a prediction target for accurate prediction. Thus, this study proposes an approach to obtain this single cluster, which recursively partitions a cluster from the previous stage of clustering, thereby enhancing the prediction accuracy of existing clustering methods. Empirical validation using the Standard and Poor’s 500 index with economic and financial variables across different time intervals demonstrated the efficacy of the proposed approach. Enhancing predictive accuracy and stability, the proposed approach based on the fundamental principles of prediction would facilitate comprehensive applicability in prediction problem.

Keywords: Clustering; Gap statistic; k-means; Prediction; Time-series (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10825-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:66:y:2025:i:4:d:10.1007_s10614-024-10825-9

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10825-9

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().