IMF Working Papers

UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification

By Jorge A Chan-Lau, Ran Wang

November 25, 2020

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Jorge A Chan-Lau, and Ran Wang. UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification, (USA: International Monetary Fund, 2020) accessed December 11, 2024

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Summary

We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be identified with a different regime. The likelihood that a country may experience an econmic crisis could be set equal to its cluster crisis frequency. Moreover, unFEAR could serve as a first step towards developing cluster-specific crisis prediction models tailored to each crisis regime.

Subject: Early warning systems, Economic and financial statistics, Financial crises, Financial statistics, Machine learning, Technology

Keywords: Autoencoder, Biased label problem, Clustering, Crisis data points, Crisis frequency, Crisis observation, Crisis prediction, Crisis risk, Deep learning, Early warning systems, Global, Machine learning, Unsupervised feature extraction, WP

Publication Details

  • Pages:

    24

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2020/262

  • Stock No:

    WPIEA2020262

  • ISBN:

    9781513561660

  • ISSN:

    1018-5941