# Recursive deep learning framework for forecasting the decadal world economic outlook

arXiv: https://arxiv.org/abs/2301.10874 · HTML: https://mlsanity.com/paper/2301.10874/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 124 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture](https://mlsanity.com/paper/1802.06338/) (2018)
- [Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition](https://mlsanity.com/paper/1406.3284/) (2014)
- [Evaluation of deep learning models for multi-step ahead time series prediction](https://mlsanity.com/paper/2103.14250/) (2021)
- [Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach](https://mlsanity.com/paper/2004.11695/) (2020)
- [How is Machine Learning Useful for Macroeconomic Forecasting?](https://mlsanity.com/paper/2008.12477/) (2020)
- [COVID-19 sentiment analysis via deep learning during the rise of novel cases](https://mlsanity.com/paper/2104.10662/) (2021)
- [Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series](https://mlsanity.com/paper/2009.12325/) (2020)
- [Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model](https://mlsanity.com/paper/1808.01560/) (2018)

## Built on by (most cited first, 1 of 1)

- [AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting](https://mlsanity.com/paper/2509.02967/) (2025)

## Abstract (arXiv)

The gross domestic product (GDP) is the most widely used indicator in macroeconomics and the main tool for measuring a country's economic output. Due to the diversity and complexity of the world economy, a wide range of models have been used, but there are challenges in making decadal GDP forecasts given unexpected changes such as emergence of catastrophic world events including pandemics and wars. Deep learning models are well suited for modelling temporal sequences and time series forecasting. In this paper, we develop a deep learning framework to forecast the GDP growth rate of the world economy over a decade. We use the Penn World Table as the data source featuring 13 countries prior to the COVID-19 pandemic, such as Australia, China, India, and the United States. We present a recursive deep learning framework to predict the GDP growth rate in the next ten years. We test prominent deep learning models and compare their results with traditional econometric models for selected developed and developing countries. Our decadal forecasts reveal that that most of the developed countries would experience economic growth slowdown, stagnation and even recession within five years (2020-2024). Furthermore, our model forecasts show that only China, France, and India would experience stable GDP growth.