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Amortized Neural Networks for Agent-Based Model Forecasting

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
Year of publication:
[2023]
Authors: Koshelev, Denis ; Ponomarenko, Alexey ; Seleznev, Sergei
Publisher: [S.l.] : SSRN
Subject: Neuronale Netze | Neural networks | Agentenbasierte Modellierung | Agent-based modeling | Prognoseverfahren | Forecasting model | Theorie | Theory | Simulation
Extent:
1 Online-Ressource (27 p)
Type of publication: Book / Working Paper
Language: English
Notes:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 25, 2023 erstellt
Other identifiers:
10.2139/ssrn.4520621 [DOI]
Classification: C11 - Bayesian Analysis ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C32 - Time-Series Models ; C45 - Neural Networks and Related Topics ; C53 - Forecasting and Other Model Applications ; C63 - Computational Techniques
Source:
ECONIS - Online Catalogue of the ZBW
Persistent link: https://ebvufind01.dmz1.zbw.eu/10014346187
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