Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

Cao, Yi; Li, Yuhua; Coleman, Sonya; Belatreche, Ammar; McGinnity, T.M.

doi:10.1109/tnnls.2014.2315042

Cited by 74 publications

(12 citation statements)

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“…Obviously, they are appropriate to model the data varying over time, and the data can be considered to be generated by the process that switches between different phases or states at different time-points. The HMM has been proved as a valuable tool in human activity recognition, speech recognition and many other popular areas [32].…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

Variational Beta Process Hidden Markov Models with Shared Hidden States for Trajectory Recognition

Zhao¹,

Zhang²,

Sun³

et al. 2021

Entropy

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Hidden Markov model (HMM) is a vital model for trajectory recognition. As the number of hidden states in HMM is important and hard to be determined, many nonparametric methods like hierarchical Dirichlet process HMMs and Beta process HMMs (BP-HMMs) have been proposed to determine it automatically. Among these methods, the sampled BP-HMM models the shared information among different classes, which has been proved to be effective in several trajectory recognition scenes. However, the existing BP-HMM maintains a state transition probability matrix for each trajectory, which is inconvenient for classification. Furthermore, the approximate inference of the BP-HMM is based on sampling methods, which usually takes a long time to converge. To develop an efficient nonparametric sequential model that can capture cross-class shared information for trajectory recognition, we propose a novel variational BP-HMM model, in which the hidden states can be shared among different classes and each class chooses its own hidden states and maintains a unified transition probability matrix. In addition, we derive a variational inference method for the proposed model, which is more efficient than sampling-based methods. Experimental results on a synthetic dataset and two real-world datasets show that compared with the sampled BP-HMM and other related models, the variational BP-HMM has better performance in trajectory recognition.

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Section: Hidden Markov Modelsmentioning

confidence: 99%

Variational Beta Process Hidden Markov Models with Shared Hidden States for Trajectory Recognition

Zhao¹,

Zhang²,

Sun³

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…For the sequence X (T ) = { x 1 , x 2 , x 3 , · · · , x t , · · · , x T } , a higher order Markov model ( n -order) can be expressed as [1,5,34,38] : (1) , P (2) , · · · , P (n ) } (5) where:…”

Section: Higher Order Markov Modelsmentioning

confidence: 99%

“…(3) Establish an n -order Markov model λ(n ) = { S , Q , P (1) , P (2) , · · · , P (n ) } in the sliding window as done in Section 2.2 .…”

Section: Establishment Of An N -Order Markov Modelmentioning

confidence: 99%

“…, and x t−1 to x t . In other words, the state of current test data point x t cannot be obtained through the transition probability matrixes P (1) , P (2) , · · · , and P (n ) . Therefore, the data point x t is regarded as an anomaly.…”

Section: Detection and Retraining Phasementioning

confidence: 99%

“…The state probability distribution of time t is usually not only related to the state of time t − 1 , but also related to the states of a period of time before time t − 1 . Therefore, the short memory property of classical Markov models is not applicable to real-world data [1] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Anomaly detection based on a dynamic Markov model

Ren

2017

Information Sciences

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a b s t r a c tAnomaly detection in sequence data is becoming more and more important in a wide variety of application domains such as credit card fraud detection, health care in medical field, and intrusion detection in cyber security. In the existing anomaly detection approaches, Markov chain techniques are widely accepted for their simple realization and few parameters. However, the short memory property of a classical Markov model ignores the interaction among data, and the long memory property of a higher order Markov model clouds the relationship between the previous data and current test data, and reduces the reliability of the model. Besides, both of these models cannot successfully describe the sequences changing with a tendency. In this paper, we propose an anomaly detection approach based on a dynamic Markov model. This approach segments sequence data by a sliding window. In the sliding window, we define the states of data according to the value of the data and establish a higher order Markov model with a proper order consequently, to balance the length of the memory property and keep up with the trend of sequences. In addition, an anomaly substitution strategy is proposed to prevent the detected anomalies from impacting the building of the models and keep anomaly detection continuously. The experimental results using simulated datasets and real-world datasets have demonstrated that the proposed approach improves the adaptability and stability of anomaly detection in sequence data.

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Order flow dynamics for prediction of order cancelation and applications to detect market manipulation

2019

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In this work, a methodology is proposed to detect and predict the intention of cancelation of a large order at an optimal event‐time horizon by analyzing real order flow market data. We achieve this by reconstructing the full history of the limit order book and formulate the case as a binary classification supervised learning problem. The results presented in this study suggest that using the information at the microstructure level of the order book is highly efficient for predicting and detecting the cancelation of the large order than using information at the macrostructure level, and that predicting the cancelation is marginally outperformed by the detection case. With this, we make a step forward in identifying potential orders related to price manipulation but the results can be used by institutional traders to anticipate adversary market impact produced by large orders.

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Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

Cited by 74 publications

References 50 publications

Variational Beta Process Hidden Markov Models with Shared Hidden States for Trajectory Recognition

Variational Beta Process Hidden Markov Models with Shared Hidden States for Trajectory Recognition

Anomaly detection based on a dynamic Markov model

Order flow dynamics for prediction of order cancelation and applications to detect market manipulation

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