The versatility of spectrum analysis for forecasting financial time series

Rostan, Pierre; Rostan, Alexandra

doi:10.1002/for.2504

Cited by 27 publications

(34 citation statements)

References 45 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accurate time series prediction is important for a wide range of areas. To determine the prediction methods and their time horizon, it is necessary to have the most accurate image of the prediction variables, nature of the data, and data availability, (Rostan and Rostan 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Vochоzka

Horák

Šuleř

2019

JRFM

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

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

Vochоzka

Horák

Šuleř

2019

JRFM

View full text Add to dashboard Cite

show abstract

“…The accurate prediction of time series is very important for a wide range of areas of competence [22]. The prediction requires a most accurate data on prognosis variables, data characteristics and data accessibility; moreover, the method of the prediction and its time horizon need to be devised [23]. Networks of radial basic functions (RBF) belong to ANNs called feedforward networks since the data in the network structure are processed in one direction -from input neurons to output neurons.…”

Section: Introductionmentioning

confidence: 99%

Using RBF neural networks to identify relationship between development of oil prices in world market and value of Chinese currency

2020

View full text Add to dashboard Cite

The objective of the contribution is to identify a possible relationship between the development of the price of Brent oil (Brent in USD/barrel) and the CNY / USD Exchange rate by means of artificial neural networks. Understanding future fluctuation characteristics and the trend in oil prices is the basis for a deep understanding of systemic mechanisms and trends in related research areas. However, given the complexities of oil prices, it is very difficult to obtain accurate forecasts. Within the experiment, a total of 50,000 artificial RBF neural networks were generated. Was found the CNY / USD price will play a significant role in creating China's real product. Given that it was already proven that the CNY / USD exchange depends on Brent in USD / barrel, it is important to focus the further research on finding out the time lag with which the price of Brent in USD / barrel is actually reflected in the price of CNY / USD.

show abstract

“…Forecasting methods used in the past include an econometric model [2], a time series model [3,4], artificial neural network and support vector machine, and hybrid methods [5]. Most of these techniques are based on historical data, but the long lag period of the predicted values often leads to problems.…”

Section: Introductionmentioning

confidence: 99%

Intelligence in Tourism Management: A Hybrid FOA-BP Method on Daily Tourism Demand Forecasting with Web Search Data

Liang

et al. 2019

Mathematics

View full text Add to dashboard Cite

The Chinese tourism industry has been developing rapidly for the past several years, and the number of people traveling has been increasing year by year. However, many problems still beset current tourism management. Lack of effective management has caused numerous problems, such as tourists stranded during tourist season and the declining service quality of scenic spots, which have become the focus of tourists’ attention. Network search data can intuitively reflect the attention of most users through the combination of the network search index and the back propagation (BP) neural network model. This study predicts the daily tourism demand in the Huangshan scenic spot in China. The filtered keyword in the Baidu index is added to the hybrid neural network, and a BP neural network model optimized by a fruit fly optimization algorithm (FOA) based on the web search data is established in this study. Different forecasting methods are compared in this paper; the results prove that compared with other prediction models, higher accuracy can be obtained when it comes to the peak season using the FOA-BP method that includes web search data, which is a sustainable means of practically solving the tourism management problem by a more accurate prediction of tourism demand of scenic spots.

show abstract

The versatility of spectrum analysis for forecasting financial time series

Cited by 27 publications

References 45 publications

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

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

Using RBF neural networks to identify relationship between development of oil prices in world market and value of Chinese currency

Intelligence in Tourism Management: A Hybrid FOA-BP Method on Daily Tourism Demand Forecasting with Web Search Data

Contact Info

Product

Resources

About