Department of Artificial Intelligence, Chongqing College of Finance and Economics, Yongchuan 402160, Chongqing, China.
Comput Intell Neurosci. 2022 Jan 30;2022:2900434. doi: 10.1155/2022/2900434. eCollection 2022.
Forecasting regional economic activity is a progressively significant element of regional economic research. Regional economic prediction can directly assist local, national, and subnational policymakers. Regional economic activity forecast can be employed for defining macroeconomic forces, such as prediction of stock market and cyclicality of national labor market movement. The recent advances of machine learning (ML) models can be employed to solve the time series prediction problem. Since the parameters involved in the ML model considerably influence the performance, the parameter tuning process also becomes essential. With this motivation, this study develops a quasioppositional cuckoo search algorithm (QOCSA) with a nonlinear support vector machine (SVM)-based prediction model, called QOCSO-NLSVM for regional economic prediction. The goal of the QOCSO-NLSVM technique is to identify the present regional economic status. The QOCSO-NLSVM technique has different stages such as clustering, preprocessing, prediction, and optimization. Besides, the QOCSO-NLSVM technique employs the density-based clustering algorithm (DBSCAN) to determine identical states depending upon the per capita NSDP growth trends and socio-economic-demographic features in a state. Moreover, the NLSVM model is employed for the time series prediction process and the parameters involved in it are optimally tuned by the use of the QOCSO algorithm. To showcase the effective performance of the QOCSO-NLSVM technique, a wide range of simulations take place using regional economic data. To determine the current economic situation in a region, the QOCSO-NLSVM technique is used. The simulation results reported the better performance of the QOCSO-NLSVM technique over recent approaches. The QOCSO-NLSVM technique generated effective results with a minimal mean square error of 70.548 or greater. Astonishingly good results were obtained using the QOCSO-NLSVM approach, which had the lowest root mean square error (RMSE) of 8.399.
预测区域经济活动是区域经济研究中一个日益重要的组成部分。区域经济预测可以直接帮助地方、国家和次国家的政策制定者。区域经济活动预测可用于确定宏观经济力量，例如预测股票市场和国家劳动力市场波动的周期性。机器学习 (ML) 模型的最新进展可用于解决时间序列预测问题。由于 ML 模型中涉及的参数对性能有很大影响，因此参数调整过程也变得至关重要。基于此动机，本研究开发了一种拟对偶布谷鸟搜索算法 (QOCSA)，并基于非线性支持向量机 (SVM) 构建了一个预测模型，称为 QOCSO-NLSVM，用于区域经济预测。QOCSO-NLSVM 技术的目标是识别当前的区域经济状况。QOCSO-NLSVM 技术有不同的阶段，如聚类、预处理、预测和优化。此外，QOCSO-NLSVM 技术使用基于密度的聚类算法 (DBSCAN) 根据各州人均 NSDP 增长趋势和社会经济人口特征来确定相同的状态。此外，NLSVM 模型用于时间序列预测过程，其涉及的参数通过 QOCSO 算法进行最佳调整。为了展示 QOCSO-NLSVM 技术的有效性能，使用区域经济数据进行了广泛的模拟。使用 QOCSO-NLSVM 技术来确定一个地区的当前经济状况。模拟结果报告了 QOCSO-NLSVM 技术优于最新方法的更好性能。QOCSO-NLSVM 技术产生了有效的结果，最小均方误差为 70.548 或更高。令人惊讶的是，使用 QOCSO-NLSVM 方法获得了非常好的结果，其均方根误差 (RMSE) 最低，为 8.399。
