Online evolving fuzzy rule-based prediction model for high frequency trading financial data stream

Gu, Xiaowei; Angelov, Plamen; Ali, Azliza Mohd; Gruver, W.A.; Gaydadjiev, Georgi

doi:10.1109/eais.2016.7502509

Cited by 15 publications

(12 citation statements)

References 14 publications

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“…The specific image scaling operation used in the proposed approach is more often called "width normalization" [3], [4], [7]. In the proposed approach, we resize the original training images from their original dimension of 28 28…”

Section: Image Scalingmentioning

confidence: 99%

“…In the proposed approach, as the size of the images is 28 , can be extracted. To improve the distinctiveness of the HOG features, we also employ a non-linear mapping function which expands the range of values of the HOG feature [10]:…”

Section: B Hog Descriptormentioning

confidence: 99%

“…ALMMo can be used for prediction [28], but also for classification. ALMMo classifiers (same as other FRB classifiers [29]) can be of 0, 1 or higher order.…”

Section: Anya Frb and Almmo Ensemblementioning

confidence: 99%

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MICE: Multi-Layer Multi-Model Images Classifier Ensemble

Angelov

2017

2017 3rd IEEE International Conference on Cybernetics (CYBCONF)

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In this paper, a new type of fast deep learning (DL) network for handwriting recognition is proposed. In contrast to the existing DL networks the proposed approach has clearly interpretable structure that is entirely data-driven and free from user-or problem-specific assumptions. It is entirely parallelizable and very efficient. First, same fundamental image transformation techniques (rotation and scaling) that are used by other existing DL methods are used to improve the generalization. The commonly used descriptors are then used to extract the global features from the training set and based on them a bank/ensemble of zero order AnYa type fuzzy rule-based (FRB) models is built through the recently introduced Autonomous Learning Multiple Model (ALMMo) method working in parallel. The final decision about the winning class label is made by a committee on the basis of the fuzzy mixture of the trained ALMMo-0 models (where "0" stands for 0 order meaning that the consequent represent a class label, a singleton, not a regression model as in the first order). The training of the proposed MICE system is very efficient and highly parallelizable. It significantly outperforms the best known methods in terms of time and is on par in terms of precision/accuracy. Critically, it offers a high level of interpretability, transparency of the classification model, full repeatability (unlike the methods that use probabilistic elements) of the results. Moreover, it allows an evolving scenario whereby the data is provided in an incremental, online manner and the system structure is being developed in parallel with the classification which opens opportunities for online and real-time applications (on a sample by sample basis). Numerical examples from the well-known handwritten digits recognition problem (MNIST) were used and the results demonstrated the very high repeatable performance after a very short training process which is in addition to the high level of interpretability, transparency.

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Section: Image Scalingmentioning

confidence: 99%

Section: B Hog Descriptormentioning

confidence: 99%

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MICE: Multi-Layer Multi-Model Images Classifier Ensemble

Angelov

2017

2017 3rd IEEE International Conference on Cybernetics (CYBCONF)

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“…An alternative approach is the use of Evolving Intelligent Systems (EIS) [ 55 ]. These have achieved great results classifying non-stationary time series [ 19 , 29 , 30 ]. The latest EIS works apply meta-cognitive scaffolding theory for tuning the learned model incrementally in what-to-learn, when-to-learn, and how-to-learn [ 56 ].…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, the notion of concept drift [ 13 ] has gained attention in this domain [ 14 ]. The Asian financial crisis in 1997 and, more recently, the great crisis in 2007–2008 have stressed the non-stationary nature and the likelihood of drastic structural or concept changes in financial markets [ 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Incremental Market Behavior Classification in Presence of Recurring Concepts

Suárez‐Cetrulo

Cervantes

Quintana

2019

Entropy

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In recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the non-stationary nature and the likelihood of drastic structural or concept changes in the markets. Traditional systems are unable or slow to adapt to these changes. Ensemble-based systems are widely known for their good results predicting both cyclic and non-stationary data such as stock prices. In this work, we propose RCARF (Recurring Concepts Adaptive Random Forests), an ensemble tree-based online classifier that handles recurring concepts explicitly. The algorithm extends the capabilities of a version of Random Forest for evolving data streams, adding on top a mechanism to store and handle a shared collection of inactive trees, called concept history, which holds memories of the way market operators reacted in similar circumstances. This works in conjunction with a decision strategy that reacts to drift by replacing active trees with the best available alternative: either a previously stored tree from the concept history or a newly trained background tree. Both mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The experimental validation of the algorithm is based on the prediction of price movement directions one second ahead in the SPDR (Standard & Poor’s Depositary Receipts) S&P 500 Exchange-Traded Fund. RCARF is benchmarked against other popular methods from the incremental online machine learning literature and is able to achieve competitive results.

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Evolving Systems: Review

Angelov

Iglesias

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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In this article, we describe the research area of evolving systems starting with a brief history, the basic concepts, and definitions and moving to classes of evolving systems. We further provide illustrative examples of evolving systems to various problems. The research area of evolving systems is closely related to adaptive systems, machine learning, mathematical modeling, data science, and computer systems. Some systems with such properties emerged at the end of last century, but the area was formed and recognized in the current century. The importance of this research – which is now a recognized niche topic with its own scientific journal (Evolving Systems), annualIEEEconferences, technical committees, etc. – is even clearer now when the era of Big Data, data streams, machine learning, and intelligence is a topic not only of research but also of publicity. The importance of evolving systems was stressed at the very beginning of its formation as a subdiscipline because a true intelligence can only be evolving. Indeed, the majority of the real processes and phenomena that people are interested in, such as modeling, predicting, classifying, controlling, or simply monitoring, are nonstationary; they are complex, nonlinear, and dynamically evolving. This includes climate, human behavior, social and biomedical systems, and even contemporary technical systems. The reality in which we are living now, couple of decades after the appearance of evolving systems, is characterized not only by a huge volume of exponentially growing data but also by their dynamically evolving nature, heterogeneous form, uncertainty, and often lack of structure. The classical theories and practical toolset (techniques, algorithms, and methods) have their own limitations and often rely on unrealistic assumptions. The concept of evolving systems was revolutionary and broke a number of these assumptions bringing the results closer to the reality of the problems, processes, and phenomena we study. One such key restrictive assumption usually made is about the fixed structure of the model of the system that describes processes or phenomena we are interested in. This is closely linked with the millennia old principle of approaching complex issues – “divide et Impera.” Adding to it “evolve” is vital.

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Online evolving fuzzy rule-based prediction model for high frequency trading financial data stream

Cited by 15 publications

References 14 publications

MICE: Multi-Layer Multi-Model Images Classifier Ensemble

MICE: Multi-Layer Multi-Model Images Classifier Ensemble

Incremental Market Behavior Classification in Presence of Recurring Concepts

Evolving Systems: Review

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