CZ GDP Prediction via neural networks and Box-Jenkins Method

Dvořáková, Lenka

doi:10.1051/shsconf/20173901005

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…For example, papers by Dzitsaki (2016) and Dzitsaki (2018) used Box-Jenkins-type models to simulate and forecast unemployment in Greece and the United States. In the studies (Ahmed, 2017;Dvorakova, 2017;Dritsaki, 2021;Eissa, 2020;Ghazo, 2021), the dynamics of the GDP in various countries was studied and predicted. The time series methodology (with diverse variations) was used to simulate and forecast inflation dynamics by the following scholas: Alderite (2020), Ahmed (2021), Dadyan (2020), Shinkarenko (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

The Dynamics of Trade Relations between Ukraine and Romania: Modelling and Forecasting

Melnychenko

Matskul

Osadcha

2022

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The article examines the monthly dynamics of exports, imports and balance of trade between Ukraine and Romania in the period from 2005 to 2021. Time series from 2015 to 2021 were used for modelling and forecasting (since the date the European Union–Ukraine Association Agreement took effect). Adequate models of the dynamics series of the Box-Jenkins methodology were built: additive models with seasonal component ARIMA (Autoregressive Integrated Moving Average) ARIMAS (or SARIMA) and Holt-Winters exponential smoothing with a dampened trend. Forecasting of exports, imports and trade balance for the fourth quarter of 2021 and first quarter of 2022 were completed. The forecast results showed a small relative error compared to the actual data. Thus, when forecasting the trade balance between countries using the Holt-Winters model, the relative prediction errors were: for October 2021 – 1.3%; for November 2021 – 2.6%; for December 2021 – 0.4%.

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Section: Literature Reviewmentioning

confidence: 99%

The Dynamics of Trade Relations between Ukraine and Romania: Modelling and Forecasting

Melnychenko

Matskul

Osadcha

2022

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Forecasting Macao GDP Using Different Artificial Neural Networks

Yang

Zhang

Cuthbert

et al. 2018

Information Science and Applications 2018

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The Ukraine and EU trade balance: prediction via various models of time series

Matskul¹,

Okara

Podvalna³

2020

SHS Web Conf.

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This article is the first to study, simulate and forecast the monthly dynamics of the trade balance between Ukraine and the European Union for the period from 2005 to 2019. In the presented work, three types of models were used for modeling and forecasting: Automated Neural Networks, additive models ARIMA *ARIMAS (Autoregressive integrated moving average with season component) and Holts model with a damped trend. When modeling using the Automated Neural Networks module, various ensembles of networks and neuron activation functions in hidden layers were used. It turned out that Automated Neural Networks have poor prognostic ability (as in the case considered by us, when modeling insufficiently long series of dynamics). Therefore, when modeling and forecasting the dynamics of the Ukraine-EU trade balance, classical (so-called Box-Jenkins) time series models were used. In this case, the time series is divided into several components (in our case, terms): the main trend is the trend, the seasonal component and the random component (the so-called white noise). By smoothing the initial series, a trend was found, and an analysis of the autocorrelation functions revealed a one-year seasonality. Eliminating the trend and the seasonal component, we obtained a random component, which has a Gaussian distribution. This made it possible to apply first the ARIMA* ARIMAS additive model, and then the Holt model of exponential smoothing with a damped trend. Adequate models of Ukraine-EU trade balance dynamics have been obtained, according to which the forecast has been made. A comparative analysis of the models used. The best model was chosen for forecasting, which allowed to get a good forecast (in comparison with actual data).

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CZ GDP Prediction via neural networks and Box-Jenkins Method

Cited by 5 publications

References 8 publications

The Dynamics of Trade Relations between Ukraine and Romania: Modelling and Forecasting

The Dynamics of Trade Relations between Ukraine and Romania: Modelling and Forecasting

Forecasting Macao GDP Using Different Artificial Neural Networks

The Ukraine and EU trade balance: prediction via various models of time series

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