Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering

DOI: http://dx.doi.org/10.15439/2023R12

Citation: Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering, Pradeep Kumar, Manuel Cardona, Vijender Kumar Solanki, Tran Duc Tan, Abdul Wahid (eds). ACSIS, Vol. 38, pages 111–114 (2023)

Full text

Abstract. This study sees how well various models can anticipate the jobless rate. The objective of the review is to find the best model for anticipating jobless rates. There is likewise the utilization of a spiral premise neural network and learning vector quantization. While learning vector quantization and an outspread premise capability brain network are utilized together, the outcomes show that none of the other foreseeing models fill in too. It likewise involves techniques like straightforward normal and backing vector relapse as a component of a gathering to obtain significantly more exact outcomes. In our task to sort out state jobless numbers, we presently utilize the SVM, Random Forest, Gradient Boosting, and Extreme Machine Learning methods. This product takes every one of the information from the picked state and uses the ML strategies referenced above to construct a preparation model. This model can then be utilized to anticipate joblessness for the following month or series.

References

1. Wei Xu School of Information, Renmin University of China, “Forecasting the Unemployment Rate by Neural Networks Using Search Engine Query Data”, [Online; accessed 2012. https://ieeexplore.ieee.org/document/6149133/
2. Yiyuan Cheng:Tao Hai:Yangbing Zheng:Baolei Li ,“Prediction Model of the Unemployment rate for nanyang in henan province based on BP neural network”, [Online; accessed 2017. [Online]. Available https://ieeexplore.ieee.org/document/8392903/
3. R. Barnichon and C. J. Nekarda, “The ins and outs of forecasting unemployment: Using labor force flows to forecast the labor market,” Brookings Papers on Economic Activity, Oct 2012. [Online]. Available: http://www.brookings.edu/~/media/Projects/BPEA / Fall-2012/2012b Barnichon.pdf? lang=en
4. Wikipedia, “Okun’s law — wikipedia, the free encyclopedia,” 2015, [Online; accessed 3-June- 2015].
5. G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks:: The state of the art,” International Journal of Forecasting, vol. 14, no. 1, pp. 35 – 62, 1998.
6. S.-C. Huang, N.-Y. Wang, T.-Y. Li, Y.-C. Lee, L.- F. Chang, and T.-H. Pan, “Financial forecasting by modified kalman filters and kernel machines,” Journal of Statistics and Management Systems, vol. 16, no. 2-03, pp. 163–176, 2013.
7. F. zkan, “Comparing the forecasting performance of neural network and purchasing power parity: The case of turkey,” Economic Modelling, vol. 31, 2013.
8. M. Fernandes, M. C. Medeiros, and M. Scharth, “Modeling and predicting the CBOE market volatility index,” Journal of Banking and Finance, vol. 40, 2014.
9. G. SZPIRO, “A search for hidden relationships: Data mining with genetic algorithms,” Computational Economics, vol. 10, no. 3, 1997.
10. A. L. Montgomery, V. Zarnowitz, R. S. Tsay, and G. C. Tiao, “Forecasting the u.s. unemployment rate,” Journal of the American Statistical Association, 1998. [Online]. Available: http: //www.tandfonline.com/doi/abs/10.1080/01621459. 1998.10473696
11. P. Rothman, “Forecasting asymmetric unemployment rates,” Review of Economics and Statistics, 1998.
12. K. G and P. SM, “Dynamic asymmetries in u.s. unemployment,” Journal of Business and Economic Statistics, 1999.
13. J. Skalin and T. Tersvirta, “Modeling asymmetries and moving equilibria in unemployment rates,” Macroeconomic Dynamics, vol. 6, 2002.
14. F. Liang, “Bayesian neural networks for nonlinear time series forecasting,” Statistics and Computing, vol. 15, no. 1, pp. 13–29, 2005.
15. E. Olmedo, “Forecasting spanish unemployment using near neighbor and neural net techniques,” Computational Economics, vol. 43, no. 2, pp. 183– 197, 2014.