Time series exponential smoothing optimal parameters finding for forecasting of currency exchange rate

2016 ◽  
Vol 33 (1) ◽  
pp. 114-127
Author(s):  
A.V. Pilyugina ◽  
◽  
A.A. Boiko ◽  
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Exponential Smoothing ◽  
Optimal Parameters ◽  
Currency Exchange ◽  
Currency Exchange Rate

Analysis of time series for Malaysian currency exchange rate to the United States currency

2021 ◽  
Author(s):  
Ting Heng Sheng ◽  
Mohd Saifullah Rusiman ◽  
Norziha Che Him ◽  
Suliadi Firdaus Sufahani ◽  
Efendi Nasibov
Keyword(s):  
United States ◽  
Time Series ◽  
Exchange Rate ◽  
The United States ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Analysis Of Time Series

scholarly journals Forecasting currency exchange rate time series with fireworks-algorithm-based higher order neural network with special attention to training data enrichment

2020 ◽  
Vol 21 (4) ◽  
Author(s):  
Kishore Kumar Sahu ◽  
Sarat Chandra Nayak ◽  
Himanshu Sekhar Behera
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Exchange Rate ◽  
Training Data ◽  
Training Dataset ◽  
Functional Link ◽  
Fireworks Algorithm ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Artificial Neural

Exchange rates are highly fluctuating by nature, thus difficult to forecast. Artificial neural networks (ANN) have proved to be better than statistical methods. Inadequate training data may lead the model to reach suboptimal solution resulting, poor accuracy as ANN-based forecasts are data driven. To enhance forecasting accuracy, we suggests a method of enriching training dataset through exploring and incorporating of virtual data points (VDPs) by an evolutionary method called as fireworks algorithm trained functional link artificial neural network (FWA-FLN). The model maintains the correlation between the current and past data, especially at the oscillation point on the time series. The exploring of a VDP and forecast of the succeeding term go consecutively by the FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other models trained similarly and produces far better prediction accuracy.

scholarly journals GDP Development of China and USA in terms of mutual sanctions and COVID-19

2021 ◽  
Vol 92 ◽  
pp. 07061
Author(s):  
Petr Šuleř ◽  
Jaromír Vrbka
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Global Economy ◽  
Market Access ◽  
Value Added ◽  
Security Issues ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
The Usa ◽  
The Impact

Research background: China’s share in the global economy has experienced a swift growth since opening up and reforming the country’s foreign policy in 1978. USA sanction on China has so far concentrated on a heap of issues including China’s enormous exchange shortfall with the U.S., currency control, constrained market access, licensed innovation robbery and security issues identified with Huawei. Also, USA sanction on China has so far lead to a decrease in exports and outflow of FDI, reduce in the inflow trade and investment, and apparently hinders the Chinese GPD growth and diminished its currency exchange rate. Purpose of the article: The aim is to predict the future development of the GDP of the China and the USA and to estimate their further development through the prism of mutual trade sanctions and COVID-19. Methods: The data collection demonstrates the course of a time series of a daily RMB exchange rate development from the beginning of 1992 to June 2020. Furthermore, it represents the time series of a quarterly development of the Chinese GDP for the same time period. Using neural networks, a regression for different variants of the time series delay in connection with the analysis of the USA sanctions is conducted. Findings & Value added: The GDP of both countries has developed over the last two years, as if sanctions had not been imposed. However, the situation is changing with COVID-19. In this case, it is clear that the impact will be more significant. US GDP will stagnate. PRC GDP will fall.

Modeling currency exchange rate time series

1986 ◽  
Vol 11 (1-4) ◽  
pp. 136-140 ◽  
Author(s):  
Promod K. Chandhok ◽  
W.Robert Terry
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Currency Exchange ◽  
Currency Exchange Rate
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