Abstract
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.
Highlights
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. e goal of the QOCSO-NLSVM technique is to identify the present regional economic status. e QOCSO-NLSVM technique has different stages such as clustering, preprocessing, prediction, and optimization
The NLSVM model is elected for the time series prediction process and the parameters involved in it are optimally tuned by the use of the QOCSO algorithm. e experimental validation of the QOCSO-NLSVM technique and the results are examined in various aspects
Mishra and Ayyub [11] introduced a DL architecture in which the hierarchical clustering analysis (HCA) is utilized for predicting growth. e presented method comprises HCA and dynamic time warping (DTW) techniques that are initially applied for identifying similar socio-economic-demographic features within a provided state and similar states according to per capita NSDP growth trends, to create a fine-tuned training dataset for predicting all the states’ NSDP per capita growth
Summary
Mishra and Ayyub [11] introduced a DL architecture in which the hierarchical clustering analysis (HCA) is utilized for predicting growth. e presented method comprises HCA and DTW techniques that are initially applied for identifying similar socio-economic-demographic features within a provided state and similar states according to per capita NSDP growth trends, to create a fine-tuned training dataset for predicting all the states’ NSDP per capita growth. Experiment results demonstrate that the LightGBM-LSTM has the highest prediction accuracy and the best ability to track stock index price trends. Chatzis et al [15] integrated distinct ML methods that were proposed with daily currency, stock, and bond data from thirty-nine countries that cover a larger spectrum of economies. It especially leverages the advantages of a sequence of techniques that includes Classifier Trees, SVM, NN, RF, XGBoost, and DNN. Is experiment result suggests that compared to standard methods, the presented KELM model that incorporates tourist volume series with Google and Baidu Index could significantly enhance the prediction performances in terms of robustness analysis and forecasting accuracy Sun et al [16] verified the cointegration relationships and Granger causality between tourist arrivals in Beijing and the internet search index. is experiment result suggests that compared to standard methods, the presented KELM model that incorporates tourist volume series with Google and Baidu Index could significantly enhance the prediction performances in terms of robustness analysis and forecasting accuracy
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