Unemployment rates forecasting using supervised neural networks

Saloni Sharma, Sanjay Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages28-33
Number of pages6
ISBN (Electronic)9781467382021
DOIs
Publication statusPublished - 08-07-2016
Event6th International Conference on Cloud System and Big Data Engineering, Confluence 2016 - Uttar Pradesh, Noida, India
Duration: 14-01-201615-01-2016

Conference

Conference6th International Conference on Cloud System and Big Data Engineering, Confluence 2016
CountryIndia
CityUttar Pradesh, Noida
Period14-01-1615-01-16

Fingerprint

Unemployment
Learning Vector Quantization
Forecasting
Radial Basis Function Neural Network
Neural Networks
Autoregressive Model
Neural networks
Multilayer Neural Network
Autoregressive Moving Average Model
Transition Model
Support Vector Regression
Vector quantization
Recurrent Neural Networks
Perceptron
Forecast
Ensemble
Continue
Model
Predict
Recurrent neural networks

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Modelling and Simulation
  • Information Systems
  • Computer Networks and Communications

Cite this

Sharma, S., & Singh, S. (2016). Unemployment rates forecasting using supervised neural networks. In Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016 (pp. 28-33). [7508042] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONFLUENCE.2016.7508042
Sharma, Saloni ; Singh, Sanjay. / Unemployment rates forecasting using supervised neural networks. Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 28-33
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Sharma, S & Singh, S 2016, Unemployment rates forecasting using supervised neural networks. in Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016., 7508042, Institute of Electrical and Electronics Engineers Inc., pp. 28-33, 6th International Conference on Cloud System and Big Data Engineering, Confluence 2016, Uttar Pradesh, Noida, India, 14-01-16. https://doi.org/10.1109/CONFLUENCE.2016.7508042

Unemployment rates forecasting using supervised neural networks. / Sharma, Saloni; Singh, Sanjay.

Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 28-33 7508042.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Sharma S, Singh S. Unemployment rates forecasting using supervised neural networks. In Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 28-33. 7508042 https://doi.org/10.1109/CONFLUENCE.2016.7508042