# Economic Recession Prediction Using Deep Neural Network

arXiv: https://arxiv.org/abs/2107.10980 · HTML: https://mlsanity.com/paper/2107.10980/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 50 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [Sequential Learning of Movement Prediction in Dynamic Environments using LSTM Autoencoder](https://mlsanity.com/paper/1810.05394/) (2018)
- [Nowcasting Recessions using the SVM Machine Learning Algorithm](https://mlsanity.com/paper/1903.03202/) (2019)

## Abstract (arXiv)

We investigate the effectiveness of different machine learning methodologies in predicting economic cycles. We identify the deep learning methodology of Bi-LSTM with Autoencoder as the most accurate model to forecast the beginning and end of economic recessions in the U.S. We adopt commonly-available macro and market-condition features to compare the ability of different machine learning models to generate good predictions both in-sample and out-of-sample. The proposed model is flexible and dynamic when both predictive variables and model coefficients vary over time. It provided good out-of-sample predictions for the past two recessions and early warning about the COVID-19 recession.