scholarly journals Machine learning forecasting of CR import from PRC in context of mutual PRC and USA sanctions

2020 ◽  
Vol 73 ◽  
pp. 01017
Author(s):  
Veronika Machová ◽  
Jan Mareček
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
International Trade ◽  
Success Rate ◽  
Future Development ◽  
Trade Restrictions ◽  
Mutual Trade ◽  
The Usa

Mutual trade restrictions between the USA and the PRC caused by the USA feeling of imbalance of trade between these two countries have significantly influenced not only the trade between these two states but also the overall atmosphere of the international trade in the last few years. The objective of the contribution is to find out whether machine learning forecasting is capable of equalizing time series so that the model effectively forecasts the future development of the time series even in the context of an extraordinary situation caused by such factors as the mutual sanctions of the USA and PRC. The dataset shows the course of the time series at monthly intervals starting from January 2000 to June 2019. There is regression carried out using neural structures. Three sets of artificial neural networks are generated. They are differ in the considered time series lag. 10,000 neural networks are generated, out of which 5 with the best characteristics are retained. The mutual USA and PRC sanctions did not affect the success rate of the machine learning forecasting of the CR import from the PRC. It is evident that the mutual sanctions shall affect the trade between the CR and the PRC.

scholarly journals Machine prediction of US imports from the PRC in the context of mutual sanctions

2020 ◽  
Vol 73 ◽  
pp. 01027
Author(s):  
Petr Šuleř ◽  
Jan Mareček
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
United States Of America ◽  
The United States ◽  
Seasonal Fluctuations ◽  
Trade Restrictions ◽  
Republic Of China ◽  
The Usa ◽  
Multilayer Perceptron Networks ◽  
Excellent Tool

The aim of this paper is to mechanically predict the import of the United States of America (USA) from the People's Republic of China (PRC). The trade restrictions of the USA and the PRC caused by the USA feeling of imbalance of trade between the two states have significantly influenced not only the trade between the two players, but also the overall climate of international trade. The result of this paper is the finding that multilayer perceptron networks (MLP) appear to be an excellent tool for predicting USA imports from the PRC. MLP networks can capture both the trend of the entire time series and its seasonal fluctuations. It also emerged that time series delays need to be applied. Acceptable results are shown to delay series of the order of 5 and 10 months. The mutual sanctions of both countries did not have a significant impact on the outcome of the machine learning prediction.

scholarly journals Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC

2020 ◽  
Vol 73 ◽  
pp. 01033
Author(s):  
Jaromír Vrbka ◽  
Marek Vochozka
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Seasonal Fluctuations ◽  
Republic Of China ◽  
The World Bank ◽  
Intelligence Models ◽  
The Usa ◽  
Artificial Intelligence Models ◽  
Artificial Neural

The paper’s objective is to propose a particular methodology to be used to regard seasonal fluctuations on balancing time series while using artificial neural networks based on the example of imports from the People's Republic of China (PRC) to the USA (US). The difficulty of forecasting the volume of foreign trade is usually given by the limitations of many conventional forecasting models. For the improvement of forecasting it is necessary to propose an approach that would hybridize econometric models and artificial intelligence models. Data for an analysis to be conducted are available on the World Bank website, etc. Information on US imports from the PRC will be used. Each forecast is given by a certain degree of probability which it will be fulfilled with. Although it appeared before the experiment that there was no reason to include the categorical variable to reflect seasonal fluctuations of the USA imports from the PRC, the assumption was not correct. An additional variable, in the form of monthly value measurements, brought greater order and accuracy to the balanced time series.

scholarly journals Comparison of neural networks and regression time series when predicting the export development from the USA to PRC

2019 ◽  
Author(s):  
Jakub Horák ◽  
Petr Šuleř ◽  
Jaromír Vrbka
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Practice Research ◽  
The Usa ◽  
Artificial Neural

Purpose – artificial neural networks are compared with mixed conclusions in terms of forecasting performance. The most researches indicate that deep-learning models are better than traditional statistical or mathematical models. The purpose of the article is to compare the accuracy of equalizing time series by means of regression analysis and neural networks on the example of the USA export to China. The aim is to show the possible uses and advantages of neural networks in practice. Research methodology – the period for which the data (USA export to the PRC) are available is the monthly balance starting from January 1985 to August 2018. First of all, linear regression as the relatively simple mathematical method is carried out. Subsequently, neural networks as the computational models used in artificial intelligence are used for regression. Findings – in terms of linear regression, the most suitable one appeared to be the curve obtained by means of the least squares methods by negative-exponential smoothing, and the curve obtained by means of the distance-weighted least squares method. In terms of neural networks, all retained structures appeared to be applicable in practice. Artificial neural networks have better representational power than traditional models. Research limitations – the simplification (quite a significant one) appears both in the cases of linear regression and regression by means of neural networks. We work only with two variables – input variable (time) and output variable (USA export to the PRC). Practical implications – in practice, the results – especially the method of artificial neural networks – can be used in the measurement and prediction of the development of exports, but especially in the short term. It can be stated that due to great simplification of the reality it isnʼt possible to predict extraordinary situations and their effect on the USA export to the PRC. Originality/Value – the article focuses on the comparison of two statistical methods, in particular, artificial intelligence is not used in such applications. However, in many economic industries, it has proven better results. It is found that artificial neural networks are able to effectively learn dependencies in and between the time series in the form of export development data.

scholarly journals The Machine Prediction of the Mutual Trade between the PRC and the Czech Republic in the Global Extraordinary Situation

2021 ◽  
Vol 92 ◽  
pp. 09006
Author(s):  
Jakub Horak ◽  
Jiri Kucera
Keyword(s):  
Time Series ◽  
International Trade ◽  
Czech Republic ◽  
Bilateral Trade ◽  
Trade Flow ◽  
Value Added ◽  
The Czech Republic ◽  
Mutual Trade ◽  
Bilateral Trade Flow ◽  
The Usa

Research background: International trade is a substantial constituent of the global and regional economic development. The analysis of mutual trade serves as a tool for a monetary expression of economic transactions between a particular country and its foreign partners for a specific period. For the Czech Republic (CR), the People’s Republic of China (PRC) is the biggest exporter and the second biggest importer. The USA, however, imposes a number of economic sanctions against the PRC that do not have any significant impact on the trade between both countries and the overall growth of the Chinese economy, yet they affect the behavior of consumers and producers both in the USA and in the PRC. Purpose of the article: The aim of this paper is to use machine learning for predicting the future values of the mutual trade between the CR and the PRC for one calendar year (i.e. 12 months). Methods: Monthly data of these two states´ import and export are used to predict bilateral trade flow. The time series begins in January 2005 and ends in April 2020. Thus, the time series contains 184 data lines. Artificial intelligence - artificial neural networks - is used to predict bilateral trade flow between the PRC and the CR. The development of trade is then compared with the mutual sanctions of the PRC and the USA. Findings & Value added: This is expected that the mutual trade balance to be negative from the perspective of the CR. COVID-19 or the sanctions imposed in the international trade will not significantly affect the development of the mutual trade between the CR and the PRC.

scholarly journals Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China

2021 ◽  
Vol 14 (2) ◽  
pp. 76
Author(s):  
Petr Suler ◽  
Zuzana Rowland ◽  
Tomas Krulicky
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Future Development ◽  
Explanatory Power ◽  
Republic Of China ◽  
China Trade ◽  
The Future ◽  
The Usa ◽  
Research Questions ◽  
Trade War

The objective of this contribution is to predict the development of the Czech Republic’s (CR) exports to the PRC (People’s Republic of China) using ANN (artificial neural networks). To meet the objective, two research questions are formulated. The questions focus on whether growth in the CR’s exports to the PRC can be expected and whether MLP (Multi-Layer Perceptron) networks are applicable for predicting the future development of the CR’s exports to the PRC. On the basis of previously obtained historical data, ANN with the best explanatory power are generated. For the purpose specified, three experiments are carried out, the results of which are described in detail. For the first, second and third experiments, ANN for predicting the development of exports are generated on the basis of a time series with a 1-month, 5-month and 10-month time delay, respectively. The generated ANN are the MLP and regression time series neural networks. The MLP turn out to be the most efficient in predicting the future development of the CR’s exports to the PRC. They are also able to predict possible extremes. It is also determined that the USA–China trade war has significantly affected the CR’s exports to the PRC.

scholarly journals Evaluation of Performance of MLP Neural Networks and RBF Neural Networks in Adjusting Time Series of the Development of the Trade Balance between the USA and the PRC

2021 ◽  
Vol 13 (2) ◽  
pp. 23-38
Author(s):  
Jaromir Vrbka ◽  
Petr Suler ◽  
Veronika Machova ◽  
Jakub Horak
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Trade Balance ◽  
The United States ◽  
Radial Basis ◽  
The Usa ◽  
Artificial Neural

Artificial neural networks are widely used for predicting values, for solving possible future problems and are able to provide various solutions in problem estimation, regression or optimisation. They are useful for predicting time series too. The aim of the paper is to analyse and evaluate the performance of multilayer neural networks (hereinafter referred to as "MLP") and neural networks of radial basis function (hereinafter referred to as "RBF) in adjusting time series on the example of the trade balance between the United States and the People's Republic of China. Regression was performed using neural structures. We generated multilayer perceptron networks and neural networks of radial basis function and we generated two sets of artificial neural networks. Time was the continuous independent variable. We determined the trade balance of the USA and the PRC as a dependent variable. We can state that due to the great simplification of reality, it is not possible to predict the emergence of extraordinary situations and their impact on the trade balance of the USA and the PRC. We can state that when an adjusted time series is derived from a single variable, time, RBFs perform better than MLPs. In order to make the prediction more accurate and its calculation easier, it seems appropriate to use RBF networks, which brings a relatively high degree of accuracy.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

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