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Forecasting economic activity using a neural network in uncertain times : Monte Carlo evidence and application to the German GDP
Holtemöller, Oliver; Kozyrev, Boris - Leibniz-Institut für Wirtschaftsforschung Halle - 2024
In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN …
Persistent link: https://www.econbiz.de/10014496850
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Application of Stationary Wavelet Support Vector Machines for the Prediction of UK Economic Recession and Expansion Periods
Giovanis, Eleftherios - 2013
Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample …
Persistent link: https://www.econbiz.de/10014039546
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Nonlinear time series and neural-network models of exchange rates between the US Dollar and major currencies
Allen, David E.; McAleer, Michael; Peiris, Shelton; … - 2015
This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models,...
Persistent link: https://www.econbiz.de/10011378229
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Nonlinear time series and neural-network models of echange rates between the US Dollar and major currencies
Allen, David E.; McAleer, Michael; Peiris, Shelton; … - In: Risks : open access journal 4 (2016) 1, pp. 1-14
This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models,...
Persistent link: https://www.econbiz.de/10011443686
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Predicting housing sales in Turkey using ARIMA, LSTM and hybrid models
Soy Temür, Ayşe; Akgün, Melek; Temür, Günay - In: Journal of business economics and management 20 (2019) 5, pp. 920-938
Having forecast of real estate sales done correctly is very important for balancing supply and demand in the housing market. However, it is very difficult for housing companies or real estate professionals to determine how many houses they will sell next year. Although this does not mean that a...
Persistent link: https://www.econbiz.de/10012175928
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A comparative analysis of alternative univariate time series models in forecasting Turkish inflation
Catik, A. Nazif; Karacuka, Mehmet - 2011
This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series … models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the …, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer …
Persistent link: https://www.econbiz.de/10009125642
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Forecasting with Nonlinear Time Series Models
Terasvirta, Timo; Kock, Anders Bredahl - 2010
purpose is mentioned as well. Forecasting with complex dynamic systems, albeit less frequently applied to economic forecasting …
Persistent link: https://www.econbiz.de/10014199417
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ARIMA and Neural Networks : An Application to the Real GNP Growth Rate and the Unemployment Rate of U.S.A
Giovanis, Eleftherios - 2009
This paper examines the estimation and forecasting performance of ARIMA models in comparison with some of the most … forecasting. We found that the best model in the case of real US GNP is the AR-GRNN and for US unemployment rate is the AR-MLP …
Persistent link: https://www.econbiz.de/10012718377
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Machine learning in energy forecasts with an application to high frequency electricity consumption data
Heilmann, Erik; Henze, Janosch; Wetzel, Heike - 2021
Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system …
Persistent link: https://www.econbiz.de/10012649104
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CDS risk premia forecasting with multi-featured deep RNNs : an application on BR[I]CS countries
Kutuk, Yasin - In: Borsa Istanbul Review 23 (2023) 6, pp. 1380-1398
Using state-of-the-art recurrent neural network architectures, this study attempts to predict credit default swap risk premia for BR[I]CS countries as accurately as possible. In the time series setting, these recurrent neural networks are ELMAN, NARX, GRU, and LSTM RNNs, considering local and...
Persistent link: https://www.econbiz.de/10014447473