# Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)

arXiv: https://arxiv.org/abs/2106.08901 · HTML: https://mlsanity.com/paper/2106.08901/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 69 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima](https://mlsanity.com/paper/1609.04836/) (2016)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Processing of missing data by neural networks](https://mlsanity.com/paper/1805.07405/) (2018)
- [Doing the impossible: Why neural networks can be trained at all](https://mlsanity.com/paper/1805.04928/) (2018)

## Built on by (most cited first, 5 of 5)

- [GDP nowcasting with artificial neural networks: How much does long-term memory matter?](https://mlsanity.com/paper/2304.05805/) (2023)
- [Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis](https://mlsanity.com/paper/2203.11872/) (2022)
- [Feasibility of nowcasting SDG indicators: a comprehensive survey](https://mlsanity.com/paper/2204.01482/) (2022)
- [Benchmarking Econometric and Machine Learning Methodologies in Nowcasting](https://mlsanity.com/paper/2205.03318/) (2022)
- [Differing Roles of Leisure and Productivity in GDP - A Machine Learning based comparative analysis of Germany and USA](https://mlsanity.com/paper/2606.01234/) (2026)

## Abstract (arXiv)

Artificial neural networks (ANNs) have been the catalyst to numerous advances in a variety of fields and disciplines in recent years. Their impact on economics, however, has been comparatively muted. One type of ANN, the long short-term memory network (LSTM), is particularly wellsuited to deal with economic time-series. Here, the architecture's performance and characteristics are evaluated in comparison with the dynamic factor model (DFM), currently a popular choice in the field of economic nowcasting. LSTMs are found to produce superior results to DFMs in the nowcasting of three separate variables; global merchandise export values and volumes, and global services exports. Further advantages include their ability to handle large numbers of input features in a variety of time frequencies. A disadvantage is the inability to ascribe contributions of input features to model outputs, common to all ANNs. In order to facilitate continued applied research of the methodology by avoiding the need for any knowledge of deep-learning libraries, an accompanying Python library was developed using PyTorch, https://pypi.org/project/nowcast-lstm/.