Amortized neural networks for agent-based model forecasting

Denis Koshelev, Alexey Ponomarenko and Sergei Seleznev

Papers from arXiv.org

Abstract: In this paper, we propose a new procedure for unconditional and conditional forecasting in agent-based models. The proposed algorithm is based on the application of amortized neural networks and consists of two steps. The first step simulates artificial datasets from the model. In the second step, a neural network is trained to predict the future values of the variables using the history of observations. The main advantage of the proposed algorithm is its speed. This is due to the fact that, after the training procedure, it can be used to yield predictions for almost any data without additional simulations or the re-estimation of the neural network

Date: 2023-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-hme and nep-net
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http://arxiv.org/pdf/2308.05753 Latest version (application/pdf)

Related works:
Working Paper: Amortized Neural Networks for Agent-Based Model Forecasting (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2308.05753

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