Statistical modeling method of human actions expressed by multi-dimentional time series data with Hidden Markov Model

2015 ◽  
Author(s):  
Kae Doki ◽  
Takahito Hirai ◽  
Akihiro Torii ◽  
Kohjiro Hashimoto ◽  
Shinji Doki
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Statistical Modeling ◽  
Time Series Data ◽  
Hidden Markov ◽  
Modeling Method ◽  
Series Data ◽  
Human Actions

scholarly journals Modelling the Dynamics of Biological Systems with the Geometric Hidden Markov Model

2017 ◽  
Author(s):  
Borislav Vangelov ◽  
Mauricio Barahona
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Time Series Data ◽  
Hidden Markov ◽  
Stem Cell Differentiation ◽  
Coarse Grained ◽  
Genetic Circuit ◽  
Series Data ◽  
Biological Processes

ABSTRACTMany biological processes can be described geometrically in a simple way: stem cell differentiation can be represented as a branching tree and cell division can be depicted as a cycle. In this paper we introduce the geometric hidden Markov model (GHMM), a dynamical model whose goal is to capture the low-dimensional characteristics of biological processes from multivariate time series data. The framework integrates a graph-theoretical algorithm for dimensionality reduction with a latent variable model for sequential data. We analyzed time series data generated by an in silico model of a biomolecular circuit, the represillator. The trained model has a simple structure: the latent Markov chain corresponds to a two-dimensional lattice. We show that the short-term and long-term predictions of the GHMM reflect the oscillatory behaviour of the genetic circuit. Analysis of the inferred model with a community detection methods leads to a coarse-grained representation of the process.

Hidden Markov Model(HMM)-Based Fishing Activity Prediction Using V-Pass Data

2021 ◽  
Vol 8 (4) ◽  
pp. 221-227
Author(s):  
Ju-Han Park ◽  
Ho-Kun Jeon ◽  
Chan-Su Yang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Time Series Data ◽  
Hidden Markov ◽  
Series Data ◽  
Automatic Identification ◽  
Identification System ◽  
Research Station ◽  
Fishing Vessel ◽  
Fishing Activity

Illegal fishing has been a serious threat to the conservation of seafood resources and provoked the importance of marine surveillance. There are several types of fishing vessel monitoring systems operated by Republic of Korea, for example, Vessel Monitoring System(VMS), Automatic Identification System (AIS), V-Pass and VHF-DSC. However, those methods are not adaptable directly to fishing activity monitoring. The limitation requires more human resources to determine fishing status. Thus, this study proposes a method of estimating fishing activity from V-Pass, fishing vessel position reporting system, using Hidden Markov Model (HMM). HMM is a model to determine status through probability distribution for a sequence of time-series data. First of all, fishing activity status was labeled on V-Pass data. The distribution of speed on fishing activity was computed from the labeled data and HMM was constructed from the data obtained at Socheongcho Ocean Research Station (SORS). The model was first applied to the data of SORS for a test, and then Busan for validation. The model showed 99.4% and 89.6% as test and validation accuracy, respectively. It is concluded that the HMM can be applicable to predict a fishing activity from vessel tracks.

scholarly journals Global Stock Selection with Hidden Markov Model

Risks ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Nguyet Nguyen ◽  
Dung Nguyen
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Time Series Data ◽  
Hidden Markov ◽  
Series Data ◽  
Stock Selection ◽  
Weighted Score ◽  
Time Periods ◽  
Composite Scores ◽  
Global Stock

Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data. In this paper, we establish a multi-step procedure for using HMM to select stocks from the global stock market. First, the five important factors of a stock are identified and scored based on its historical performances. Second, HMM is used to predict the regimes of six global economic indicators and find the time periods in the past during which these indicators have a combination of regimes that is similar to those predicted. Then, we analyze the five stock factors of the All country world index (ACWI) in the identified time periods to assign a weighted score for each stock factor and to calculate the composite score of the five factors. Finally, we make a monthly selection of 10% of the global stocks that have the highest composite scores. This strategy is shown to outperform those relying on either ACWI, any single stock factor, or the simple average of the five stock factors.

Literature Review on Hidden Markov Model-Based Sequential Data Clustering

2015 ◽  
Vol 713-715 ◽  
pp. 1750-1756 ◽  
Author(s):  
Ming Ji ◽  
Fei Wang ◽  
Jia Ning Wan ◽  
Yuan Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Time Series Data ◽  
Hidden Markov ◽  
Distance Measures ◽  
Series Data ◽  
Future Research ◽  
Sequential Data ◽  
Parametric Methods ◽  
Model Based

The purpose of this report is to investigate current existing algorithm to cluster sequential data based on hidden Markov model (HMM). Clustering is a classic technique that divides a set of objects into groups (called clusters) so that objects in the same cluster are similar in some sense. The clustering of sequential or time series data, however, draws lately more and more attention from researchers. Hidden Markov model (HMM)-based clustering of sequences is probabilistic model-based approach to clustering sequences. Generally, there are two kinds of methodologies: parametric and semi-parametric. The parametric methods make strict assumptions that each cluster is represented by a corresponding HMM, while the semi-parametric approaches relax this assumption and transform the problem to a similarity-based issue. Generally, the semi-parametric methods perform better than parametric approaches as reported by some researchers. Future research can be done in exploring new distance measures between sequences and extending current HMM-based methodologies by using other models.

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