Enhanced Model for Recession Forecasting Using Artificial Neural Network

@article{1704486,
      author = {Ike, Uche Kingsley, Ajaero, Grace Ngozi PhD, Nnadozie, Benedicta Ozioma, Okorie, Juliet Ijeoma},
      title = {Enhanced Model for Recession Forecasting Using Artificial Neural Network},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {852-863},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17044863.pdf},
      abstract = {The aim of this research project is to develop a neural network multi-layer architecture based on back propagation algorithm to predict recession probability in Nigeria. Recession probability forecasting has proven a tedious task for economists, particularly those at investment banks. The motivation for carrying out this study hinges on the drawbacks of the prevalent forecasting methods which include statistical regression analysis or linear models which are strenuous and highly inefficient when dealing with extra-large datasets.  Recession as a national economic situation is the result of complex phenomena, whose effects translates into a blend of gains or losses that appear in a market time-series that is usually predicted by extrapolation. Dataset spanning ten years representative of economic recession indicators were analyzed using a feed forward neural network with back propagation algorithm and K-means clustering algorithm. The Object-oriented Methodology was adopted for analysis and implementation was carried out in Google Colab which has Python programming language at its core. The developed system is found to predict an economic recession more accurately when compared with other models, so that adequate economic policies can be made to tackle national economic recession.},
      keywords = {Artificial Neural Network, Recession, Forecasting},
      month = {May},
  }