Search results

1 – 10 of over 1000
Article
Publication date: 3 July 2020

Xiaoyun Ye and Myung-Mook Han

By using a new feature extraction method on the Cert data set and using a hidden Markov model (HMM) to model and analyze the behavior of users to distinguish whether the behavior…

Abstract

Purpose

By using a new feature extraction method on the Cert data set and using a hidden Markov model (HMM) to model and analyze the behavior of users to distinguish whether the behavior is normal within a continuous period.

Design/methodology/approach

Feature extraction of five parts of the time series by rules and sorting in chronological order. Use the obtained features to calculate the probability parameters required by the HMM model and establish a behavior model for each user. When the user has abnormal behavior, the model will return a very low probability value to distinguish between normal and abnormal information.

Findings

Generally, HMM parameters are obtained by supervised learning and unsupervised learning, but the hidden state cannot be clearly defined. When the hidden state is determined according to the data set, the accuracy of the model will be improved.

Originality/value

This paper proposes a new feature extraction method and analysis mode, which determines the shape of the hidden state according to the situation of the data set, making subsequent HMM modeling simple and efficient and in turn improving the accuracy of user behavior detection.

Details

Information & Computer Security, vol. 30 no. 1
Type: Research Article
ISSN: 2056-4961

Keywords

Book part
Publication date: 8 June 2011

Nazli Turan, Miroslav Dudik, Geoff Gordon and Laurie R. Weingart

Purpose – The purpose of this chapter is to introduce new methods to behavioral research on group negotiation.Design/methodology/approach – We describe three techniques from the…

Abstract

Purpose – The purpose of this chapter is to introduce new methods to behavioral research on group negotiation.

Design/methodology/approach – We describe three techniques from the field of Machine Learning and discuss their possible application to modeling dynamic processes in group negotiation: Markov Models, Hidden Markov Models, and Inverse Reinforcement Learning. Although negotiation research has employed Markov modeling in the past, the latter two methods are even more novel and cutting-edge. They provide the opportunity for researchers to build more comprehensive models and to use data more efficiently. To demonstrate their potential, we use scenarios from group negotiation research and discuss their hypothetical application to these methods. We conclude by suggestions for researchers interested in pursuing this line of work.

Originality/value – This chapter introduces methods that have been successfully used in other fields and discusses how these methods can be used in behavioral negotiation research. This chapter can be a valuable guide to researchers that would like to pursue computational modeling of group negotiation.

Article
Publication date: 20 May 2021

Jianfei Li, Bei Li, Kun Tang and Mengxia Sun

Based on the analysis of the dissipative structure of the retail service supply chain (RSSC), this paper divides the system into two internal and external dissipative mechanisms…

Abstract

Purpose

Based on the analysis of the dissipative structure of the retail service supply chain (RSSC), this paper divides the system into two internal and external dissipative mechanisms, including the internal performance dissipation mechanism and the perceived quality dissipation mechanism outside the system. Based on the prediction of RSSC performance, this paper aims to discuss the application of Hidden Markov Model (HMM) in this field and puts forward a set of complete process of forecasting the service supply chain (SSC) performance based on HMM model.

Design/methodology/approach

Based on the theory of dissipative structure, this paper selects the RSSC as the research object, analyzes the system characteristics of the dissipation structure of RSSC from three aspects, such as system opening type, distance from equilibrium state and nonlinear order and describes the quality fluctuation process of RSSC as a Hidden Markov process. Taking the RSSC of J Company as an example, this paper makes use of the observed state value of customer perceived service quality from 1997 to 2016, predicts the performance status of the enterprise's RSSC.

Findings

The research results show that: RSSC is a dissipative structure system, and its performance is the internal entropy flow of the system, and the customer perceived service quality is external, their interaction determines the dynamic evolution of the system dissipation structure, and the Markov property between supply chain performance and perceived service quality. There is a Markov property between supply chain performance and perceived service quality. Using the perceived service quality observation state data of the external consumers of the system can effectively predict the implicit state of RSSC performance. Based on this prediction result, the strategy adjustment and optimization of the action mechanism of internal and external entropy flow in the dissipative structure system can be carried out to promote the sustainable development of the RSSC.

Originality/value

This paper thinks that RSSC is a dissipative structure system and the SSC performance and customer perceived service quality are the internal and external entropy flow of the system, which determines the dynamic evolution of the system dissipation structure. There is a Markov property between supply chain performance and perceived service quality. The hidden state of SSC performance can be predicted effectively by using a hidden Markov model and observing state data of perceived service quality from consumers outside the system.

Book part
Publication date: 2 November 2009

Ole Rummel

This chapter presents a model of distribution dynamics in the presence of measurement error in the underlying data. Studies of international growth convergence generally ignore…

Abstract

This chapter presents a model of distribution dynamics in the presence of measurement error in the underlying data. Studies of international growth convergence generally ignore the fact that per capita income data from the Penn World Table (PWT) are not only continuous variables but also measured with error. Together with short-time scale fluctuations, measurement error makes inferences potentially unreliable. When first-order, time-homogeneous Markov models are fitted to continuous data with measurement error, a bias towards excess mobility is introduced into the estimated transition probability matrix. This chapter evaluates different methods of accounting for this error. An EM algorithm is used for parameter estimation, and the methods are illustrated using data from the PWT Mark 6.1. Measurement error in income data is found to have quantitatively important effects on distribution dynamics. For instance, purging the data of measurement error reduces estimated transition intensities by between one- and four-fifths and more than halves the observed mobility of countries.

Details

Measurement Error: Consequences, Applications and Solutions
Type: Book
ISBN: 978-1-84855-902-8

Article
Publication date: 23 November 2021

Kwansoo Kim, Sang-Yong Tom Lee and Saïd Assar

The authors examine cryptocurrency market behavior using a hidden Markov model (HMM). Under the assumption that the cryptocurrency market has unobserved heterogeneity, an HMM…

1156

Abstract

Purpose

The authors examine cryptocurrency market behavior using a hidden Markov model (HMM). Under the assumption that the cryptocurrency market has unobserved heterogeneity, an HMM allows us to study (1) the extent to which cryptocurrency markets shift due to interactions with social sentiment during a bull or bear market and (2) the heterogeneous pattern of cryptocurrency market behavior under these two market conditions.

Design/methodology/approach

The authors advance the HMM model based on two six-month datasets (from November 2017 to April 2018 for a bull market and from December 2018 to May 2019 for a bear market) collected from Google, Twitter, the stock market and cryptocurrency trading platforms in South Korea. Social sentiment data were collected by crawling Bitcoin-related posts on Twitter.

Findings

The authors highlight the reaction of the cryptocurrency market to social sentiment under a bull and a bear market and in two hidden states (an upward and a downward trend). They find: (1) social sentiment is relatively relevant during a bull compared to a bear market. (2) The cryptocurrency market in a downward state, that is, with a local decreasing trend, tends to be more responsive to positive social sentiment. (3) The market in an upward state, that is, with a local increasing trend, tends to better interact with negative social sentiment.

Originality/value

The proposed HMM model contributes to a theoretically grounded understanding of how cryptocurrency markets respond to social sentiment in bull and bear markets through varied sequences adjusted for cryptocurrency market heterogeneity.

Details

Industrial Management & Data Systems, vol. 122 no. 2
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 28 February 2023

Safaa Kadhem and Haider Thajel

One of the most important sources of energy in the world, due to its great impact on the global economy, is the crude oil. Due to the instability of oil prices which exhibit…

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Abstract

Purpose

One of the most important sources of energy in the world, due to its great impact on the global economy, is the crude oil. Due to the instability of oil prices which exhibit extreme fluctuations during periods of different times of market uncertainty, it became hard to the governments to predict accurately the prices of crude oil in order to build their financial budgets. Therefore, this study aims to analyse and model crude oil price using the hidden Markov process (HMM).

Design/methodology/approach

Traditional mathematical approaches of time series may be not give accurate results to measure and analyse the crude oil price, since the latter has an unstable and fluctuating nature, hence, its prediction forms a challenge task. A novel methodology that is so-called the HMM is proposed that takes into account the heterogeneity in prices as well as their hidden state-based behaviour.

Findings

Using the Bayesian approach, several estimated models with different ranks are fitted to a non-homogeneous data of Iraqi crude oil prices from January 2010 into December 2021. The model selection criteria and measures of the prediction performance of each model are applied to choose the best model. Movements of crude oil prices exhibit extreme fluctuations during periods of different times of market uncertainty. The processes of model estimation and the model selection were conducted in Python V.3.10, and it is available from the first author on request.

Originality/value

Using the Bayesian approach, several estimated models with different ranks are fitted to a non-homogeneous data of Iraqi crude oil prices from January 2010 to December 2021.

Details

The Journal of Risk Finance, vol. 24 no. 2
Type: Research Article
ISSN: 1526-5943

Keywords

Article
Publication date: 24 August 2010

S.L. Chan and W.H. Ip

The paper aims to propose a novel strategic approach, named a Scorecard‐Markov model, combining an evaluation scorecard and a hidden Markov model (HMM) for new product idea…

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Abstract

Purpose

The paper aims to propose a novel strategic approach, named a Scorecard‐Markov model, combining an evaluation scorecard and a hidden Markov model (HMM) for new product idea screening (NPIS) decisions.

Design/methodology/approach

A scorecard is constructed to evaluate new product ideas on several criteria, including customer needs, marketing strength, competency, manufacturing compatibility, and distribution channels, involving a consideration of risk buy. A HMM is then developed accordingly to predict the overall performance of new ideas in terms of success probability. To implement the model, it is trained and tested by the historical dataset of a world‐class, leading company in the power tools industry through a case study.

Findings

The approach is proven to be encouraging and meaningful. The scorecard can serve as a guide for new product idea evaluation to convert experts' linguistic judgments to quantifiable and comparable data, whereas the HMM can determine the success probability of new product ideas to support NPIS decision making based on their computed evaluation performance. The optimal cut‐off value for making either a go or kill decision on each idea can thus be determined. Concerning the case company, a go decision should be made when the probability lies in the interval [0.53, 1].

Practical implications

The model can prevent companies from undertaking risky and failed new product development projects. Further, it is believed that this study can assist decision makers in choosing winning new product ideas towards commercialization in an effective and certain manner, thus enhancing the new product success rate in the innovation industry.

Originality/value

The approach incorporating the scorecard method and HMM is novel. Illustrated by the case study, the application of this approach to NPIS decisions is confirmed to be effective.

Details

Industrial Management & Data Systems, vol. 110 no. 7
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 11 August 2023

Kala Nisha Gopinathan, Punniyamoorthy Murugesan and Joshua Jebaraj Jeyaraj

This study aims to provide the best estimate of a stock's next day's closing price for a given day with the help of the hidden Markov model–Gaussian mixture model (HMM-GMM). The…

Abstract

Purpose

This study aims to provide the best estimate of a stock's next day's closing price for a given day with the help of the hidden Markov model–Gaussian mixture model (HMM-GMM). The results were compared with Hassan and Nath’s (2005) study using HMM and artificial neural network (ANN).

Design/methodology/approach

The study adopted an initialization approach wherein the hidden states of the HMM are modelled as GMM using two different approaches. Training of the HMM-GMM model is carried out using two methods. The prediction was performed by taking the closest closing price (having a log-likelihood within the tolerance range) to that of the present one as the closing price for the next day. Mean absolute percentage error (MAPE) has been used to compare the proposed GMM-HMM model against the models of the research study (Hassan and Nath, 2005).

Findings

Comparing this study with Hassan and Nath (2005) reveals that the proposed model outperformed in 66 out of the 72 different test cases. The results affirm that the model can be used for more accurate time series prediction. Further, compared with the results of the ANN model from Hassan's study, the proposed HMM model outperformed 24 of the 36 test cases.

Originality/value

The study introduced a novel initialization and two training/prediction approaches for the HMM-GMM model. It is to be noted that the study has introduced a GMM-HMM-based closing price estimator for stock price prediction. The proposed method of forecasting the stock prices using GMM-HMM is explainable and has a solid statistical foundation.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 17 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 16 August 2022

Xiaoyi Sylvia Gao, Imran S. Currim and Sanjeev Dewan

This paper aims to demonstrate how consumer clickstream data from a leading hotel search engine can be used to validate two hidden information processing stages – first eliminate…

Abstract

Purpose

This paper aims to demonstrate how consumer clickstream data from a leading hotel search engine can be used to validate two hidden information processing stages – first eliminate alternatives, then choose – proposed by the revered information processing theory of consumer choice.

Design/methodology/approach

This study models the two hidden information processing stages as hidden states in a hidden Markov model, estimated on consumer search behavior, product attributes and diversity of alternatives in the consideration set.

Findings

First, the stage of information processing can be statistically characterized in terms of consumer search covariates, including trip characteristics, use of search tools and the diversity of the consideration set, operationalized in terms of: number of brands, dispersion of price and dispersion of quality. Second, users are more sensitive to price and quality in the first rather than the second stage, which is closer to purchase.

Research limitations/implications

The results suggest practical implications for how search engine managers can target consumers with appropriate marketing-mix actions, based on which information processing stage consumers might be in.

Originality/value

Most previous studies on validating the information processing theory of consumer choice have used laboratory experiments, subjects and information display boards comprising hypothetical product alternatives and attributes. Only a few studies use observational data. In contrast, this study uniquely uses point-of-purchase clickstream data on actual visitors at a leading hotel search engine and tests the theory based on real products, attributes and diversity of the consideration set.

Details

European Journal of Marketing, vol. 56 no. 8
Type: Research Article
ISSN: 0309-0566

Keywords

Article
Publication date: 23 December 2019

Mahua Bhowmik and P. Malathi P. Malathi

Cognitive radio (CR) plays a very important role in enabling spectral efficiency in wireless communication networks, where the secondary user (SU) allows the licensed primary…

Abstract

Purpose

Cognitive radio (CR) plays a very important role in enabling spectral efficiency in wireless communication networks, where the secondary user (SU) allows the licensed primary users (PUs). The purpose of this paper is to develop a prediction model for spectrum sensing in CR.

Design/methodology/approach

This paper proposes a hybrid prediction model, called krill-herd whale optimization-based actor critic neural network and hidden Markov model (KHWO-ACNN-HMM). The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs. For better sensing, the eigenvalue based on cooperative sensing used in CR. Finally, a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing. The predicted results of KHWO-ACNN and HMM are combined by a fusion model, for which a weighted entropy fusion is employed to determine the free spectrum available in CRs.

Findings

The performance of the prediction model is evaluated based on metrics, such as probability of detection, probability of false alarm, throughput and sensing time. The proposed spectrum sensing method achieves maximum probability of detection of 0.9696, minimum probability of false alarm rate as 0.78, minimum throughput of 0.0303 and the maximum sensing time of 650.08 s.

Research implications

The proposed method is useful in various applications, including authentication applications, wireless medical networks and so on.

Originality/value

A hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models. The proposed hybrid model outperformed the other techniques.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

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