Fuzzy Time Series Forecasting With a Probabilistic Smoothing Hidden Markov Model

Cheng, Yi-Chung; Li, Sheng-Tun

doi:10.1109/tfuzz.2011.2173583

Cited by 51 publications

(15 citation statements)

References 45 publications

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“…This study investigates the effectiveness of a hybrid approach. Cheng and Li (2012) combined fuzzy time series forecasting with a probabilistic smoothing hidden Markov model. Denkena and Nemeti (2013) have developed stock market related pricing mechanisms for the tool and mould manufacturing industry.…”

Section: Review Of Literaturementioning

confidence: 99%

An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

2019

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This paper explores the power of stock trading trend using an integrated New Threshold Fuzzy Cognitive Maps (NTFCMs) Markov chain model. This new model captures the positive as well as the negative jumps and predicts the trend for different stocks over 4 months which follow an uptrend, downtrend and a mixed trend. The mean absolute per cent error (MAPE) tolerance limits, the root mean square error (RMSE) tolerance limits are determined for various stock indices over a multi-timeframe period and observed for the existing methods lying within the defined limits. The results show for every 'n' number of predictions made, the predicted close value of the day's stock price was within tolerance limit with 0 % error and with 100% accuracy in predicting the future trend.

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Section: Review Of Literaturementioning

confidence: 99%

An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

2019

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“…The minimum description length (MDL) and piecewise aggregate approximation (PAA) sequence of real number were used to reduce the dimension in [362], [363]. Real sequence dependency mining and sequence prediction based on HMM was proposed in [364], [365]. Real number sequence correlation analysis and event diagnosis were introduced in [366], and [367] analyzed the hypothesis bias in the classification of real number sequences.…”

Section: ) Mobile Terminal Behavior Analysis Of Network Usersmentioning

confidence: 99%

Behavior Analysis for Electronic Commerce Trading Systems: A Survey

et al. 2019

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With the rapid development of the Internet and the continuous progress of computer science and technology, Internet-based online trading has developed rapidly and improved the convenience of everyday life. Increasingly, people conduct business activities through online transactions and payment methods, and the development prospects for online transactions are quite broad. In addition, e-commerce trading systems have attracted considerable attention. Many studies have been conducted to analyze the behaviors of these systems. This paper focuses on analyzing both system behavior and user behavior in e-commerce trading systems. To reflect the characteristics of online trading systems, system behavior reflects the strength of the system, while user behavior reflects various user actions. Consistency, which is a basis for determining whether two objects are similar, provides a reference for designers and modelers. This paper also focuses on analyzing the consistency of system behavior, especially regarding the measurement and diagnosis of e-commerce trading systems. Then, we study the existing user behavior analysis methods for e-commerce trading systems. We divide user behaviors into four groups: web browsing behavior, keystroke behavior, network transaction behavior, and mobile terminal behavior. The performance and correlation analysis techniques of these methods are discussed and compared in detail. Finally, we provide ideas for future research involving behavior analysis for online trading systems.

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“…By extending the values of model parameters into intervals, there is room for HMM algorithms to perform better. The methods in [15], [16] need the representation of fuzzy relationship or fuzzy rules and are less cost-effective than the proposed work. Zeng [17] presents a fuzzy-set method to allow randomness in HMM and achieves robust performance on speech variation, but a Gaussian primary membership function has to be used for each state.…”

Section: Background and Related Workmentioning

confidence: 99%

Twitter part-of-speech tagging using pre-classification Hidden Markov model

Sun

Liu

Lin

et al. 2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-Hidden Markov models (HMM) have been widely used in natural language processing (NLP), especially in syntactic level applications, which appears naturally as short-rangedependent sequence recognition problems. But the structure of HMM limits the usage of global knowledge including the sentiment analysis of the text, which has become an increasingly popular research topic in NLP now. In this paper, we propose a novel treatment of HMM model to use the result of sentimental subjectivity analysis in syntactic level task, i.e. part-of-speech (POS) tagging. The subjectivity information is introduced as a pre-classification procedure into the interval-type HMM. The subjectivity degree of the testing sentence is used as a combination factor to choose an appropriate value from the interval. Experiments results on public tagging data sets shows that the proposed approach enhanced the performance of POS tagging.

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Fuzzy Time Series Forecasting With a Probabilistic Smoothing Hidden Markov Model

Cited by 51 publications

References 45 publications

An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

Behavior Analysis for Electronic Commerce Trading Systems: A Survey

Twitter part-of-speech tagging using pre-classification Hidden Markov model

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