Unemployment Rates Forecasting Using Supervised Neural Networks

Sharma, Saloni and Singh, Sanjay (2016) Unemployment Rates Forecasting Using Supervised Neural Networks. In: International Conference on Cloud System and Big Data Engineering, 14/01/2016, Amity University, Noida, UP.

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This study investigates the efficiency of various models used to forecast unemployment rates. The objective of the study is to find the model which most accurately predicts the unemployment rates. It starts with auto regressive models like autoregressive moving average model and smooth transition auto regressive model and then continues to explore four types of neural networks, namely multi layer perceptron, recurrent neural network, psi sigma neural network and radial basis function neural network. In addition to these, it also uses learning vector quantization in a combination with radial basis neural network. The results have shown that the combination of learning vector quantization and radial basis function neural network outperforms all the other forecasting models. It further uses ensemble techniques like support vector regression, simple average, to give even more accurate results

Item Type: Conference or Workshop Item (Paper)
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 20 Jul 2016 15:47
Last Modified: 20 Jul 2016 15:47
URI: http://eprints.manipal.edu/id/eprint/146674

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