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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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An enhanced bat algorithm based intelligent inspired architecture for resilient macroeconomic prediction.

Sirong Mou¹, Junqi Gan², Yanze Yang³

¹The National University of Malaysia, UKM, Bangi, 43600, Selangor, Malaysia.

Scientific Reports

|November 21, 2025

Summary

This study introduces an Enhanced Bat Algorithm-Backpropagation Neural Network (EBA-BPNN) model to improve macroeconomic forecasting accuracy. The EBA-BPNN model significantly reduces errors in GDP predictions, offering better policy support.

Keywords:

Backpropagation Neural Network Bat Algorithm Dynamic inertia weight Economic Prediction

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Area of Science:

Economics
Computational Intelligence
Data Science

Background:

Macroeconomic forecasting with nonlinear, high-dimensional data is an NP-hard problem.
Traditional models struggle with local optima, limiting forecast accuracy and reliability.
Accurate forecasting is crucial for policy formulation and global risk management.

Purpose of the Study:

To develop a hybrid model, EBA-BPNN, for optimizing Backpropagation Neural Networks (BPNN) in macroeconomic forecasting.
To address the limitations of traditional models in handling complex economic data.
To enhance forecast accuracy and reliability for better economic policy support.

Main Methods:

Utilized Dynamic Time Warping (DTW) for cross-country economic cycle alignment.
Employed Granger causality and mutual information for core variable selection (32 variables).
Optimized BPNN using an Enhanced Bat Algorithm (EBA) with dynamic inertia weights, Cauchy-Gaussian perturbation, and gradient-assisted search.

Main Results:

EBA-BPNN reduced Mean Absolute Error (MAE) in quarterly GDP forecasting by 29.3% compared to BPNN.
Achieved MAE reductions of 17.8% vs. PSO-BPNN and 15.6% vs. BA-BPNN.
Maintained limited MAE increase (12.3%) even under extreme economic scenarios.

Conclusions:

The EBA-BPNN model provides a high-precision approach for dynamic economic modeling.
Offers significant improvements over existing methods for macroeconomic forecasting.
Supports evidence-based fiscal policy formulation and cross-border investment decisions.