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Inoue, A., Jin, L., & Rossi, B. (2017). Rolling Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters. Journal of Economoetrics, 196, 55-67.
https://doi.org/10.1016/j.jeconom.2016.03.006

has been cited by the following article:

• TITLE: Adaptive Financial Fraud Detection in Imbalanced Data with Time-Varying Poisson Processes

  AUTHORS: Régis Houssou, Jérôme Bovay, Stephan Robert

  KEYWORDS: Homogeneous Poisson Process, Inhomogeneous Poisson Process, Intensity Model, Fraud Detection, Imbalanced Data

  JOURNAL NAME: Journal of Financial Risk Management, Vol.8 No.4, December 30, 2019

  ABSTRACT: This paper discusses financial fraud detection in imbalanced dataset using homogeneous and non-homogeneous Poisson processes. The probability of predicting fraud on the financial transaction is derived. Applying our methodology to financial datasets with different fraud profiles shows a better predicting power than a baseline approach, especially in the case of higher imbalanced data.