ISSN2320-2882
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Paper Title
Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes
Authors
Vanathi Munusamy , Gopalakrishnan Raji , Dr.K.Alagirisamy , Vinoth Raman
Keywords
Ensemble learning, uncertainty quantification, conformal prediction, quantile regression, nonlinear forecasting, Monte Carlo simulation
Abstract
The study provides an examination of the ensemble machine learning algo rithms alongside the techniques of uncertainty measures on nonlinear economic forecasting. We have an extremely large simulation experiment: we have 12 data generating processes (DGPs), which are threshold effects, smooth transition autoregressive models, regime switching dynamics, and chaotic systems. Together with conformal prediction and quantile regression forests, there are three ensemble methods, which include bagging, boosting, and stacking, that pro vide powerful prediction intervals. We perform performance appraisal of Monte Carlo replications of forecasting with sample sizes of 250 to 2000 observations and signal-noise ratio of 10,000 and nonlinearity of the signal of different levels with signal-noise ratios. Results have depicted stacking with conformal prediction to have great coverage probabilities (93.2-96.8) and competitive point forecast accuracy. Boosting performs optimally in high signal to noises circumstances, but loses coverage when nonlinearity is severe. Bagging achieves good baseline performance of all DGPs. Our analysis provides the outcome of the strong dominance regions of both ensembles techniques and provides effective suggestions of quantifying uncertainty in applying economic forecasting techniques. The study is also relevant methodologically by hypothetically proposing an adaptive ensemble selection framework and empirically by defining performance limits at both extremes within the non-linearity spectrum.
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IJCRT — journal style
"Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 5, page no.c353-c363, May 2026, Available :https://ijcrt.org/papers/IJCRT2605282.pdf
APA — 7th edition
Munusamy, V., Raji, G., Dr.K.Alagirisamy, & Raman, V. (2026). Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes. International Journal of Creative Research Thoughts, 14(5), c353-c363. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605282
MLA — 9th edition
Munusamy, Vanathi, et al. "Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes." International Journal of Creative Research Thoughts, vol. 14, no. 5, 2026, pp. c353-c363, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605282.
Chicago — 17th, bibliography
Munusamy, Vanathi, et al. "Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes." International Journal of Creative Research Thoughts 14, no. 5 (2026): c353-c363. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605282.
Harvard — author–date
Munusamy, V. et al. (2026) 'Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes', International Journal of Creative Research Thoughts, 14(5), pp. c353-c363. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605282
IEEE — numbered reference
V. Munusamy, G. Raji, Dr.K.Alagirisamy and V. Raman, "Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes," IJCRT, vol. 14, no. 5, pp. c353-c363, May. 2026.
Vancouver — biomedical
Munusamy V, Raji G, Dr.K.Alagirisamy, Raman V. Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes. IJCRT. 2026 May;14(5):c353-c363.
AMA — 11th edition
Munusamy V, Raji G, Dr.K.Alagirisamy, Raman V. Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes. IJCRT. 2026;14(5):c353-c363. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605282
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2605282,
author = {Vanathi Munusamy and Gopalakrishnan Raji and Dr.K.Alagirisamy and Vinoth Raman},
title = {Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {5},
pages = {c353-c363},
year = {2026},
month = {May},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605282}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Munusamy, Vanathi
AU - Raji, Gopalakrishnan
AU - Dr.K.Alagirisamy
AU - Raman, Vinoth
TI - Ensemble Machine Learning with Uncertainty Quantification for Nonlinear Economic Forecasting: A large-scale simulation experiment across multiple data generating processes
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 5
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605282
SP - c353-c363
ER -
Issue
Volume 14 Issue 5,
May-2026
Pages : c353-c363
Article Information
- Pages
- c353-c363
- Published
- May 2026
- Registration ID
- IJCRT_307614
- Published Paper ID
- IJCRT2605282
- Research Area
- Other area not in list
- Author Type
- Indian Author
- Location
- Salem, Tamilnadu, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 164
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605282
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2605282.pdf
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