TURBaN: A Theory-Guided Model for Unemployment Rate Prediction Using Bayesian Network in Pandemic Scenario

Document Type

Conference Article

Publication Title

Lecture Notes in Networks and Systems

Abstract

Unemployment rate is one of the key contributors that reflect the economic condition of a country. Accurate prediction of unemployment rate is a critically significant as well as demanding task which helps the government and the policymakers to make vital decisions. Though the recent research thrust is primarily towards hybridization of various linear and non-linear models, these may not perform satisfactorily well under the circumstances of unexpected events, e.g., during sudden outbreak of any infectious disease. In this paper, we explore this fact with respect to the current scenario of coronavirus disease (COVID) pandemic. Further, we show that explicit Bayesian modeling of pandemic impact on unemployment rate, together with theoretical insights from epidemiological models, can address this issue to some extent. Our developed theory-guided model for unemployment rate prediction using Bayesian network (TURBaN) is evaluated in terms of predicting unemployment rate in various states of India under COVID-19 pandemic scenario. The experimental result demonstrates the efficacy of TURBaN, which outperforms the state-of-the-art hybrid techniques in majority of the cases.

First Page

521

Last Page

531

DOI

10.1007/978-3-031-27409-1_47

Publication Date

1-1-2023

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