Application of Hurst Exponent (H) and the R/S Analysis in the Classification of FOREX Securities

Raimundo, Milton Saulo; Okamoto, Jun

doi:10.7763/ijmo.2018.v8.635

Cited by 38 publications

(22 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Any deviations can and should generate an alert, e.g., to the unauthorized access to our network. This problem is described in detail in the publications [1,2,20,25,27]. In the case of Hurst values above 0.5, the methods examined proved to be successful, but the discrepancy of results for each of them was so great that we have shown that not all of them can be used to compare to each other.…”

Section: Research Methodology and R Environmentmentioning

confidence: 96%

See 1 more Smart Citation

Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks

Dymora

Mazurek

2020

Applied Sciences

View full text Add to dashboard Cite

This study aimed to determine the applicability of using selected libraries of computing environment R to establish the coefficient of self-similarity. R environment is an analytical environment with rich functionality that is used in many research and practical works concerning data analysis and knowledge discovery. Such an issue is significant in the context of contemporary wide area computer networks and the emerging type of network infrastructure IoT. This originates directly from the new nature of IoT traffic, which also has a substantial impact on Industry 4.0. It provides built-in operations facilitating data processing. The Hurst coefficient is used to evaluate traffic behavior and analyze its character. The study analyzed two cases of IoT network traffic: high and low intensity. For different sizes of time windows, we dermined the statistical Hurst exponent and compared it with standard, smoothed, and Robinson methods. The accuracy of the methods used was evaluated. Spectral regression graphs were additionally generated for selected motion variants. The obtained results were verified by Higuchi and Aggvar methods.

show abstract

Section: Research Methodology and R Environmentmentioning

confidence: 96%

“…The Hurst exponent values of time series can be estimated using the rescaled range analysis (R/S) method. The method used for the Hurst exponent estimated calculation, using the R/S analysis, estimated by simple linear regression, follows the methodology developed by Mandelbrot and Wallis [27,28].…”

Section: Self-similarity Statistical Factormentioning

confidence: 99%

Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks

Dymora

Mazurek

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The calculation of false neighbors was calculated in the R environment using the fractal library. To determine the fractal dimension of the studied series , the Hurst index is first calculated according to the following algorithm [15]: -determination of the average value of the time series -construction of the linear regression equation, the coefficient of an independent variable in which is the Hurst indicator. Then the fractal dimension of the time series is .…”

Section: Ifmentioning

confidence: 99%

Recurrence Analysis of the Economic Behavior of Agents of the Frequency of Online Exchange Rate in the Information Environment

Kucherova¹,

Ocheretin

Los

et al. 2020

Proceedings of the III International Scientific Congress Society of Ambient Intelligence 2020 (ISC-SAI 2020)

View full text Add to dashboard Cite

The behavior of the socioeconomic agents of the foreign exchange market has always been characterized by insufficient predictability, therefore today researchers do not leave the search for new methods and indicators for their prediction. With the development of the information society and online technologies, one of the manifestations of the behavior of agents in the foreign exchange market has become their semantic-thematic online queries in search engines. Considering that the time series of exchange rates have been sufficiently studied, insufficient attention has been paid to the dynamics of the frequency of requests for exchange rates as to the parameters of the online behavior of foreign exchange market agents. Since the dynamics of the frequency of requests for exchange rates (dollar, euro) is non-linear, a recurrence analysis was applied. The results of this analysis proved that the tendencies of informational activity of agents are characterized by an antipersistent type of behavior with periodicity and the presence of drift, which are manifested in varying degrees for each exchange rate. If for the indicator of the dollar exchange rate a concentration of interest of market agents near the trajectory of a certain attractor with other deviations due to other factors is shown, then a similar indicator of the euro exchange rate is characterized by a more pronounced and stable over time cyclical manifestation of information activity of agents. In further researches, it is planned to implement a quantitative analysis of the constructed recurrence plots in order to take into account the behavior of the agents of the foreign exchange market.

show abstract

“…This specific task is involved in many approaches for data analysis, pattern detection, judicial scrutiny of iconic images and information repossession. Clustering is hence called an ample gismo for data analysis in bioinformatics, marketing, scrutiny of images and so on [5,6,7,8]. In addition, many basic queries arise during the research, such as which algorithm should be the best fit on datasets and which algorithm will be used sparingly under which necessary conditions.…”

Section: Introductionmentioning

confidence: 99%

A Euclidean Group Assessment on Semi-Supervised Clustering for Healthcare Clinical Implications Based on Real-Life Data

Sohail

Ren

Muhammad

2019

IJERPH

View full text Add to dashboard Cite

The grouping of clusters is an important task to perform for the initial stage of clinical implication and diagnosis of a disease. The researchers performed evaluation work on instance distributions and cluster groups for epidemic classification, based on manual data extracted from various repositories, in order to evaluate Euclidean points. This study was carried out on Weka (3.9.2) using 281 real-life health records of diabetes mellitus patients including males and females of ages>20 and <87, who were simultaneously suffering from other chronic disease symptoms, in Nigeria from 2017 to 2018. Updated plugins of K-mean and self-organizing map(SOM) machine learning algorithms were used to cluster the data class of mellitus type for initial clinical implications. The results of the K-mean assessment were built in 0.21 seconds with nine iterations for “type” and eight for “class” attributes. Out of 281 instances, 87 (30.97%) were classified as negative and 194 (69.03%) as positive in the testing on the Euclidean space plot. By assessment for Euclidean points, SOM discovered the search space in a more effective way, but K-mean positioning potencies are impulsive in convergence. This study is important for epidemiological disease diagnosis in countries with a high epidemic risk and low socioeconomic status.

show abstract

Application of Hurst Exponent (H) and the R/S Analysis in the Classification of FOREX Securities

Cited by 38 publications

References 27 publications

Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks

Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks

Recurrence Analysis of the Economic Behavior of Agents of the Frequency of Online Exchange Rate in the Information Environment

A Euclidean Group Assessment on Semi-Supervised Clustering for Healthcare Clinical Implications Based on Real-Life Data

Contact Info

Product

Resources

About