Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/53596
Title: Quantum Neural Networks for Forecasting Inflation Dynamics
Authors: Alaminos, David
Esteban, Ignacio
Salas, M Belén
Callejón, Angela M
Keywords: Inflation dynamics;Neural Networks;Quantum Computing;Quantum Neural Networks;Macroeconomic forecasting
Issue Date: Feb-2020
Publisher: NISCAIR-CSIR, India
Abstract: Inflation is a key indicator in the economy that measures the average level of prices of goods and services, being an important ratio in public and private decision-making, so predicting it with precision has always been a concern of economists. This paper makes inflation predictions with different time horizons applying quantum theory through Quantum Neural Networks. The results obtained teach that Quantum Neural Networks overcome the predictive power of the existing models in the previous literature and yields a low-level of errors when predicting any change in the direction of the forecast trend.
Page(s): 103–106
URI: http://nopr.niscair.res.in/handle/123456789/53596
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.79(02) [February 2020]

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