scholarly journals Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance

2019 ◽  
Vol 61 ◽  
pp. 01031 ◽  
Author(s):  
Jaromír Vrbka ◽  
Zuzana Rowland ◽  
Petr Šuleř
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
International Relations ◽  
Trade Balance ◽  
Point Of View ◽  
Republic Of China ◽  
China Trade ◽  
The Neural Networks ◽  
Time Series Alignment ◽  
The Eu

China, by GDP, is the second largest economic power, and hence also a key player in the field of international relations. As far as the EU is concerned, it is China's largest trading partner. From this point of view, it is clear that monitoring export and import development between these partners is essential. This paper therefore aims to compare two useful methods, namely the accuracy of time series alignment through regression analysis and artificial neural networks, to assess the evolution of the EU and the People's Republic of China trade balance. Data on the export and import trends of these two partners since 2000 have been used, and it is clear that the trade balance was completely different that year than it is now. The development over time is interesting. The most appropriate curve is selected from the linear regression, and from the neural networks three useful neural structures are selected. We also look at the prediction of future developments while taking into account seasonal fluctuations.

scholarly journals Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance

2019 ◽  
Vol 61 ◽  
pp. 01023 ◽  
Author(s):  
Zuzana Rowland ◽  
Petr Šuleř ◽  
Marek Vochozka
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Czech Republic ◽  
Linear Regression ◽  
Trade Balance ◽  
People’S Republic Of China ◽  
People's Republic Of China ◽  
The Czech Republic ◽  
Republic Of China ◽  
China Trade

Foreign trade has been and is considered to be very important. Trade balance measurement provides one of the best analyzes of a country's external economic relations. It serves as a monetary expression of economic transactions between a certain country and its foreign partners over a certain period. The aim of this paper is to compare the accuracy of time series alignment by means of regression analysis and neural networks on the example of the trade balance of the Czech Republic and the People's Republic of China. Trade balance data between the Czech Republic and the People's Republic of China is used. This is a monthly balance starting in 2000 and ending in July 2018. First, a linear regression is made followed by regression using artificial neural networks. A comparison of both methods at expert level and experience of the evaluator, the economist, is performed. Optically, the LOWESS curve appears to be best out of the linear regression and the fifth preserved RBF 1-24-1 network seems the mot appropriate out of neural networks.

scholarly journals Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations

2020 ◽  
Vol 73 ◽  
pp. 01032
Author(s):  
Marek Vochozka ◽  
Zuzana Rowland
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Czech Republic ◽  
Trade Balance ◽  
Seasonal Fluctuation ◽  
Seasonal Fluctuations ◽  
Republic Of China ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks

The objective of the contribution is to introduce a methodology for considering seasonal fluctuations in equalizing time series using artificial neural networks on the example of the Czech Republic and the People´s Republic of China trade balance. The data available is the data on monthly balance for the period between January 2000 and July 2018, that is, 223 input data. The unit is Euro. The data for the analysis are available on the World Bank web pages etc. Regression analysis is carried out using artificial neural networks. There are two types on neural networks generated, multilayer perceptron networks (MLP) and radial basis function networks (RBF). In order to achieve the optimal result, two sets of neural structures are generated. There are generated a total of 10,000 neural structures, out of which only 5 with the best characteristics are retained. Finally, the results of both groups of retained neural networks are compared. The contribution this paper brings is the involvement of variables that are able to forecast a possible seasonal fluctuation in the time series development when using artificial neural networks. Moreover, neural networks have been identified that achieve slightly better results than other networks, specifically these are the neural networks 1. MLP 13-6-1 and 3. MLP 13-8-1.

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 Using Artificial Neural Networks for Equalizing Time Series Considering Seasonal Fluctuations

2019 ◽  
Vol 71 ◽  
pp. 01003
Author(s):  
J. Vrbka ◽  
J. Horák ◽  
V. Machová
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Seasonal Fluctuations ◽  
Republic Of China ◽  
The People ◽  
Data Points ◽  
Artificial Neural ◽  
The Individual

The objective of this contribution is to prepare a methodology of using artificial neural networks for equalizing time series when considering seasonal fluctuations on the example of the Czech Republic import from the People´s Republic of China. If we focus on the relation of neural networks and time series, it is possible to state that both the purpose of time series themselves and the nature of all the data are what matters. The purpose of neural networks is to record the process of time series and to forecast individual data points in the best possible way. From the discussion part it follows that adding other variables significantly improves the quality of the equalized time series. Not only the performance of the networks is very high, but the individual MLP networks are also able to capture the seasonal fluctuations in the development of the monitored variable, which is the CR import from the PRC.

scholarly journals Export performance and competitiveness in wood products in the Czech Republic and Austria

2019 ◽  
Vol 70 (5) ◽  
pp. 743-764
Author(s):  
Nahanga Verter ◽  
Libor Grega
Keyword(s):  
Time Series ◽  
Czech Republic ◽  
Trade Balance ◽  
Export Performance ◽  
Single Market ◽  
Wood Products ◽  
Export Diversification ◽  
The Czech Republic ◽  
Balance Index ◽  
The Eu

This article assesses the development of wood exports in the Czech Republic (Czechia) and Austria in recent years. Some approaches, such as revealed comparative advantage (RCA), relative trade balance index (RTB), and diversification ratios are used to assess the export performance and competitiveness indicators in these countries. The RCA result reveals that both Czechia and Austria have been competitive in the global wood markets, just as the countries have witnessed positive in RTB within the period under study. Market diversification results indicate that both countries concentrated in few markets (mainly within the EU single market) for exports of wood products. Also, the competitiveness based on the product structure shows that both countries, notably Austria wood product groups have been diversified and mostly processed before exports. In summary, both countries have performed impressively within the period under study even though the time series for the research was short. Nevertheless, there is a need for market export diversification beyond the EU’s single market.

scholarly journals A balanced approach: Brexit from political and business perspectives

2019 ◽  
Vol 13 (1) ◽  
pp. 804-816
Author(s):  
Răzvan Hoinaru ◽  
Mihnea Năstase
Keyword(s):  
European Union ◽  
International Relations ◽  
Value At Risk ◽  
Risk Sharing ◽  
Integrated Approach ◽  
Point Of View ◽  
Business Strategies ◽  
The European Union ◽  
The Uk ◽  
The Eu

Abstract There is a considerable amount of publications written on rolling back the EU supra state, national sovereignty regain, and strategic (mis)conceptions for analysing Brexit scenarios for both the UK and the EU. Many articles present a unilateral point of view with a tendency to be normative. The presentation of only one-sided political, historical, and business perspectives can be very dangerous, limiting understanding and constructive approaches. This also happens with macro-economic analyses that are used fit for purpose. David Cameron’s political calculation to call for a referendum regarding the UK’s withdrawal from the European Union has had complex ramifications. With causes that have led to the British citizens’ decision that range from multiple crises in the European Union, member states’ inability for burden and risk sharing, to the lack of trust portrayed by European institutions and a confusing internal rhetoric. With a City of London remaining undecided and continuously evaluating the value at risk of Brexit, and in the absence of a new European financial center, it is important to make sense of the arguments of both in and out supporters. Thus, this article attempts to present a more integrated approach, spanning across politics, trade, private businesses and social attitudes. This paper looks beyond international relations between nations and takes into consideration the international relations between corporations and their business strategies.

scholarly journals Machine learning forecasting of CR and PRC balance of trade

2020 ◽  
Vol 73 ◽  
pp. 01004
Author(s):  
Tomàš Brabenec ◽  
Petr Šuleř
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Data Set ◽  
Republic Of China ◽  
The People ◽  
The Future ◽  
Balance Of Trade ◽  
Political Economic ◽  
Social Importance

International trade is an important factor of economic growth. While foreign trade has existed throughout the history, its political, economic and social importance has grown significantly in the last centuries. The objective of the contribution is to use machine learning forecasting for predicting the balance of trade of the Czech Republic (CR) and the People´s Republic of China (PRC) through analysing and machine learning forecasting of the CR import from the PRC and the CR export to the PRC. The data set includes monthly trade balance intervals from January 2000 to June 2019. The contribution investigates and subsequently smooths two time series: the CR import from the PRC; the CR export to the PRC. The balance of trade of both countries in the entire monitored period is negative from the perspective of the CR. A total of 10,000 neural networks are generated. 5 neural structures with the best characteristics are retained. Neural networks are able to capture both the trend of the entire time series and its seasonal fluctuations, but it is necessary to work with time series lag. The CR import from the PRC is growing and it is expected to grow in the future. The CR export to the PRC is growing and it is expected to grow in the future, but its increase in absolute values will be slower than the increase of the CR import from the PRC.

scholarly journals Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

2019 ◽  
Vol 61 ◽  
pp. 01006 ◽  
Author(s):  
Jakub Horák ◽  
Tomáš Krulický
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Stock Prices ◽  
Stock Price ◽  
Exponential Time ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Artificial Neural ◽  
Time Series Alignment

Accurate stock price prediction is very difficult in today's economy. Accurate prediction plays an important role in helping investors improve return on equity. As a result, a number of new approaches and technologies have logically evolved in recent years to predict stock prices. One is also the method of artificial neural networks, which have many advantages over conventional methods. The aim of this paper is to compare a method of exponential time series alignment and time series alignment using artificial neural networks as tools for predicting future stock price developments on the example of the company Unipetrol. Time series alignment is performed using artificial neural networks, exponential alignment of time series, and then a comparison of time series of predictions of future stock price trends predicted using the most successful neural network and price prediction calculated by exponential time series alignment is performed. Predictions for 62 business days were obtained. The realistic picture of further possible development is surprisingly given based on the exponential alignment of time series.

scholarly journals Exploring geometrical patterns in streamflow time series: utility for forecasting?

2018 ◽  
Vol 49 (6) ◽  
pp. 1724-1739 ◽  
Author(s):  
Ramesh S. V. Teegavarapu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
The United States ◽  
Data Driven ◽  
Storm Events ◽  
Streamflow Prediction ◽  
One Step ◽  
The Neural Networks ◽  
Ann Models ◽  
Improved Performance

Abstract Streamflow time series often provide valuable insights into the underlying physical processes that govern responses of any watershed to storm events. Patterns derived from time series based on repeated structures within these series can be beneficial for developing new or improved data-driven forecasting models. Data-driven models, artificial neural networks (ANN), are developed in the current study for streamflow prediction using input structures that are classified by geometrically similar patterns. A new modular and integrated ANN architecture that combines multiple ANN models, referred to as pattern-classified neural network (PCNN), is proposed, developed and investigated in this study. The PCNN relies on the development of several independent local models instead of one global data-driven prediction model. The PCNN models are evaluated for one step-ahead prediction of daily streamflows for Reed Creek and Little River, Virginia, and Elkhorn Creek, Kentucky in the United States. Results obtained from this study suggest that the use of these patterns has improved the performance of the neural networks in prediction. The improved performance of the PCNN models can be attributed to prior classification of data benefiting generalization abilities. PCNN model outputs can also provide an ensemble of forecasts that help quantify forecast uncertainty.

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