Estimation of the development of Czech Koruna to Chinese Yuan exchange rate using artificial neural networks

Through time series analysis, it is possible to obtain significant statistics and other necessary data characteristics. Prediction of time series allows predicting future values based on previously observed values. The exact prognosis of the time series is very important for a number of different areas, such as transport, energy, finance, economics, etc. It is within the topic of economy that the analysis and prediction of time series can also be used for exchange rates. The exchange rate itself can greatly affect the whole foreign trade. The aim of this article is therefore to analyze the exchange rate development of two currencies by analyzing time series through artificial neural networks. Experimental results show that neural networks are potentially usable and effective for exchange rate prediction.

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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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Predicting the exchange rate in Sudan using neural networks models during the period (1960 - 2017): التنبؤ بسعر الصرف في السودان باستخدام نماذج الشبكات العصبية خلال الفترة (1960م – 2017م)

المجلة العربية للعلوم ونشر الأبحاث مجلة العلوم الاقتصادية والإدارية والقانونية ◽

10.26389/ajsrp.c300420 ◽

2020 ◽

Vol 4 (14) ◽

Author(s):

Fathi Ahmed Ali Adam, Mahmoud Mohamed Abdel Aziz Gamal El-Di

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Exchange Rate ◽

Network Models ◽

Neural Network Models ◽

Classical Models ◽

Artificial Neural ◽

The Exchange Rate ◽

Long Term Forecasting ◽

Artificial Neural Network Models

The study examined the use of artificial neural network models to predict the exchange rate in Sudan through annual exchange rate data between the US dollar and the Sudanese pound. This study aimed to formulate the models of artificial neural networks in which the exchange rate can be predicted in the coming period. The importance of the study is that it is necessary to use modern models to predict instead of other classical models. The study hypothesized that the models of artificial neural networks have a high ability to predict the exchange rate. Use models of artificial neural networks. The most important results ability of artificial neural networks models to predict the exchange rate accurately, Form MLP (1-1-1) is the best model chosen for that purpose. The study recommended the development of the proposed model for long-term forecasting.

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EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS

New Mathematics and Natural Computation ◽

10.1142/s1793005705000056 ◽

2005 ◽

Vol 01 (01) ◽

pp. 79-107 ◽

Cited By ~ 12

Author(s):

MAK KABOUDAN

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Exchange Rate ◽

Random Walk ◽

Genetic Programming ◽

Exchange Rates ◽

Us Dollar ◽

Artificial Neural ◽

Japanese Yen ◽

The One

Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

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Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

The Scientific World JOURNAL ◽

10.1155/2016/6709352 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Narayanan Manikandan ◽

Srinivasan Subha

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Exchange Rates ◽

Stock Prices ◽

Time Series Data ◽

Financial Analysis ◽

Parallel Implementation ◽

Series Data ◽

Artificial Neural

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

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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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