Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets

Hendricks, Dieter

doi:10.1016/j.patrec.2017.06.026

Cited by 11 publications

(8 citation statements)

References 28 publications

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“…Kullanıcıya ait click verisi analizi [6], saldırı tespit sistemleri [7][8][9], sosyal medya [10][11][12], finansal uygulamalar [13], bilimsel araştırmalar [14], sağlık araştırmaları [15][16][17], mobil uygulamalar [18], nesnelerin interneti (IoT) [19] ve sensor ağ [20,21] gibi pek çok alanda kullanılmaktadır. Nesnelerin interneti konusunun yaygınlaştığı günümüzde uygulama alanlarının daha da artacağını söylemek mümkündür.…”

Section: Akan Veri Kümeleme Yaklaşımlarının Uygulama Alanlarıunclassified

Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

Şenol

Karacan

2018

European Journal of Science and Technology

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Öz Günümüz teknolojisinin gelişmesine paralel olarak bilgisayar ortamına aktarılmış olan veri miktarı inanılmaz boyutlara ulaşmış ve gün geçtikçe de artmaktadır. Bu nedenle veriyi işleme yöntemleri de değişmektedir. Klasik kümeleme yaklaşımlarında veri statiktir. Oysa günümüz teknolojisinde, verinin çok hızlı olduğu dünyada artık veriyi akarken kümeleyecek, kullanıcıya istediği zaman sonuç verebilecek uygulamalara ihtiyaç vardır. Bu anlamda ihtiyacı karşılayan akan veri kümeleme yaklaşımlarına olan talep gün geçtikçe artmaktadır. Çünkü akan veri kümeleme yaklaşımları bir defa okumalı, hızlı ve kendisini yeni gelen veriye uyarlama özelliğine sahiptir. Yani veri bir yandan akarken bir yandan kullanıcıya sonuç üretilebilmektedir. Bu çalışmada akan veri kümeleme alanında yapılan çalışmalar derlenmekte ve bu alana ilgi duyan araştırmacılara ışık tutulmaktır.

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Section: Akan Veri Kümeleme Yaklaşımlarının Uygulama Alanlarıunclassified

Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

Şenol

Karacan

2018

European Journal of Science and Technology

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“…A speed improvement on the compute time of the realised covariance, or realised correlation matrix, potentially allows more time for learning algorithm convergence and identification. This can be of particular importance for learning algorithms that require many updates to identify a reliable optimal relationship between actions and system states given an objective, such as Q-learning based implementations of reinforcement learning for trading [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Malliavin-Mancino estimators implemented with non-uniform fast Fourier transforms

Chang,

Pienaar,

Gebbie

2020

Preprint

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We implement and test kernel averaging Non-Uniform Fast-Fourier Transform (NUFFT) methods to enhance the performance of correlation and covariance estimation on asynchronously sampled event-data using the Malliavin-Mancino Fourier estimator. The methods are benchmarked for Dirichlet and Fejér Fourier basis kernels. We consider test cases formed from Geometric Brownian motions to replicate synchronous and asynchronous data for benchmarking purposes. We consider three standard averaging kernels to convolve the event-data for synchronisation via over-sampling for use with the Fast Fourier Transform (FFT): the Gaussian kernel, the Kaiser-Bessel kernel, and the exponential of semi-circle kernel. First, this allows us to demonstrate the performance of the estimator with different combinations of basis kernels and averaging kernels. Second, we investigate and compare the impact of the averaging scales explicit in each averaging kernel and its relationship between the time-scale averaging implicit in the Malliavin-Mancino estimator. Third, we demonstrate the relationship between time-scale averaging based on the number of Fourier coefficients used in the estimator to a theoretical model of the Epps effect. We briefly demonstrate the methods on Trade-and-Quote (TAQ) data from the Johannesburg Stock Exchange to make an initial visualisation of the correlation dynamics for various time-scales under market microstructure.

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“…Akan veri kümeleme yaklaşımı söz konusu bu veriyi değerli bilgiye çevirme konusunda pek çok açıdan ihtiyacı karşılamaktadır. Akan veri kümeleme tıklama verisi [6], saldırı tespit sistemleri [7][8][9], finansal uygulamalar [10], bilimsel araştırmalar [11], sağlık araştırmaları [12][13][14], nesnelerin interneti (IoT) [15] ve mobil uygulamalar [16] gibi pek çok alanda kullanılmaktadır [17][18][19].…”

Section: Gi̇ri̇ş (Introduction)unclassified

“…The results showed that KD-AR Stream algorithm has a good clustering performance within a reasonable time by comparison with the other algorithms. 10.17341/gazimmfd.467226…”

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confidence: 99%

Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering

Şenol

Karacan

2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Highlights:Graphical/Tabular Abstract  Fully online data stream clustering  Evolutionary based clustering  Adaptive radius  Time based summarization  Memory for past status of clusters Figure A. Comparision of clustering quality and run-time complexity of algorithms on KDD datasetPurpose: The aim of this article to propose a new data stream clustering algorithm, which has an adaptive radius, can adapt itself to the evolutionary structure of streaming data and works in a fully online manner. Theory and Methods:In this study, kd-tree is used to forming and splitting clusters, adaptive radius approach is used to support increasing and decreasing the size of clusters, active/inactive status of clusters is used to adapt to the evolutionary structure of streaming data and all the operations are done online. In order to create a new cluster, the data that does not belong to any cluster are placed in a kd-tree, and the rangesearch operation is performed on those data according to predefined variables r (the radius of candidate cluster) and N (the number of data must be in the area). After forming the clusters, the radius of each cluster could be increased or decreased over time if necessary. Some clusters may be split and some may be merged over time because of dynamically changing structure of streaming data. Inactivation and reactivation of the status of clusters is used to allow for the identification of clusters formed in the same region at a different time interval with same cluster labels in accordance with the nature of the streaming data contrary to literature. This feature increases clustering quality of the proposed method. A summarization method that consist of time window and sliding window is used to support time based summarization without reduce performance. Results:To verify the effectiveness of KD-AR Stream algorithm, it is compared with SE-Stream, DPStream, and CEDAS on a variety of well-known datasets in terms of clustering quality and run-time complexity. The results show that KD-AR Stream outperforms other algorithms with a higher clustering success in a reasonable time as shown in Fig. A. Conclusion:The aim of this study is to propose a novel data stream clustering algorithm that adapts to the dynamic structure of the streaming data. The aim achieved by using the five evolutionary process which are appearance, activation/inactivation, self-evolution, merge, and split. According to the results, the proposed method is very successful in terms of clustering quality and run-time complexity.

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Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets

Cited by 11 publications

References 28 publications

Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

Malliavin-Mancino estimators implemented with non-uniform fast Fourier transforms

Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering

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