Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People´s Republic of China trade balance

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.

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Using Artificial Neural Networks for Equalizing Time Series Considering Seasonal Fluctuations

SHS Web of Conferences ◽

10.1051/shsconf/20197101003 ◽

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.

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

SHS Web of Conferences ◽

10.1051/shsconf/20207301033 ◽

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.

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

SHS Web of Conferences ◽

10.1051/shsconf/20196101031 ◽

2019 ◽

Vol 61 ◽

pp. 01031 ◽

Cited By ~ 9

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.

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

SHS Web of Conferences ◽

10.1051/shsconf/20196101023 ◽

2019 ◽

Vol 61 ◽

pp. 01023 ◽

Cited By ~ 8

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.

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Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate

Journal of Risk and Financial Management ◽

10.3390/jrfm12020076 ◽

2019 ◽

Vol 12 (2) ◽

pp. 76 ◽

Cited By ~ 7

Author(s):

Marek Vochozka ◽

Jakub Horák ◽

Petr Šuleř

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Exchange Rate ◽

Analysis Data ◽

Categorical Variables ◽

Seasonal Fluctuations ◽

Long Run ◽

Artificial Neural ◽

The Exchange Rate

The exchange rate is one of the most monitored economic variables reflecting the state of the economy in the long run, while affecting it significantly in the short run. However, prediction of the exchange rate is very complicated. In this contribution, for the purposes of predicting the exchange rate, artificial neural networks are used, which have brought quality and valuable results in a number of research programs. This contribution aims to propose a methodology for considering seasonal fluctuations in equalizing time series by means of artificial neural networks on the example of Euro and Chinese Yuan. For the analysis, data on the exchange rate of these currencies per period longer than 9 years are used (3303 input data in total). Regression by means of neural networks is carried out. There are two network sets generated, of which the second one focuses on the seasonal fluctuations. Before the experiment, it had seemed that there was no reason to include categorical variables in the calculation. The result, however, indicated that additional variables in the form of year, month, day in the month, and day in the week, in which the value was measured, have brought higher accuracy and order in equalizing of the time series.

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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

Littera Scripta ◽

10.36708/littera_scripta2020/2/3 ◽

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.

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ARTIFICIAL NEURAL NETWORKS APPLICATION IN TIME SERIES WATER DISCHARGE FOR THE PREDICTION OF FLOODING IN MOHAWK RIVER, NEW YORK

10.1130/abs/2019se-327791 ◽

2019 ◽

Author(s):

Katerina Tsakiri ◽

◽

Antonios E Marsellos ◽

Stelios Kapetanakis

Keyword(s):

Neural Networks ◽

New York ◽

Time Series ◽

Artificial Neural Networks ◽

Water Discharge ◽

Artificial Neural

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

Atmospheric Measurement Techniques ◽

10.5194/amt-9-53-2016 ◽

2016 ◽

Vol 9 (1) ◽

pp. 53-62 ◽

Cited By ~ 13

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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