Open Access Subscription Access
Quantum Neural Networks for Forecasting Inflation Dynamics
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.
Inflation Dynamics, Neural Networks, Quantum Computing, Quantum Neural Networks, Macroeconomic Forecasting.
- Ülke V, Sahin A & Subasi A, A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA, Neural Comput Appl, 30 (5) (2018), 1519-1527.
- Acosta M A, Machine learning core inflation, Econ Lett, 169(C) (2018), 47-50.
- Duncan R, & Martínez-García E, New perspectives on forecasting inflation in emerging market economies: An empirical assessment, Int J Forecasting, 35(3) (2019), 1008-1031.
- Medeiros M C, Vasconcelos G F R, Veiga A, & Zilberman E, Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods. Journal of Business & Economic Statistics, (2019), Latest Articles.
- Hassani H, & Silva, E S, Forecasting UK consumer price inflation using inflation forecasts. Research in Economics, 72(3) 2018, 367-378.
- Wan KH, Dahlsten O, Kristjánsson H, Gardner R, & Kim M S, Quantum generalisation of feed forward neural networks, NPJ Quantum Information, 3(36) (2017).
- Gonçalves C PS, Quantum Neural Machine Learning: Theory and Experiments. Chapter 5, Artificial Intelligence-Applications in Medicine and Biology. Intech Open, London (2019).
- Mahajan R P, A Quantum Neural Network Approach for Portfolio Selection, Int J Comput Appl, 29(4) (2011), 47-54.
- Zidan M, Abdel-Aty A-H, El-shafei M, Feraig M, Al-Sbou Y, Eleuch H, & Abdel-Aty M, Quantum Classification Algorithm Based on Competitive Learning Neural Network and Entanglement Measure, Appl Sci,9 (2019), 1277.
- Curiel J E, Jalón, L D, Ureba, S F & Menéndez, J A R, Neural network analysis for hotel service design in Madrid: the 3Ps methodology and the frontline staff, Tour Manage Stud, 14(SI1) (2018), 83-94.
- Saravanan P & Kalpana P. A Novel Approach to Attack Smartcards Using Machine Learning Method, J Sci Ind Res, 76 (2017), 95-99.
- Osornio-Rios R A, Identification of Positioning System for Industrial Applications using Neural Network, J Sci Ind Res, 76(3) (2017), 141-144.
- Dubrovin V T, Gabbasov F G, Chebakova V Y & Fadeeva M S, The limit theorem for dependent Beernoulli tests, IOP Conf. Series: Journal of Physics: Conf Series, 1158 (2019) 022036.
Abstract Views: 1
PDF Views: 0