In this paper, we mainly establish an optimal weighted Markov model to predict the GDP of Hunan Province from 2017 to 2023. The new model is composed of a fractional grey model and a quadratic function regression model weighted combination and is obt...
In this work, the Customer behaviour hidden Markov model (CBHMM) is proposed to predict the behaviour of customers in e-commerce with the goal to forecast the store income. The model consists of three sub-models: Vendor, Psychology and Loyalty, retur...
In this paper, a Leaky Integrate-and-Fire (LIF) model for the membrane potential of a neuron is considered, in case the potential process is a semi-Markov process. Semi-Markov property is obtained here by means of the time-change of a Gauss-Markov pr...
We introduce a bivariate Markov chain counting process with contagion for modelling the clustering arrival of loss claims with delayed settlement for an insurance company. It is a general continuous-time model framework that also has the potential to...
This paper introduces a novel kind of factorial hidden Markov model (FHMM), specifically the feedforward FHMM (FFHMM). In contrast to traditional FHMMs, the FFHMM is capable of directly utilizing supplementary information from observations through pr...
The detection and prediction of sea clutter power is the basis of inversing atmospheric duct. At present, the technology of atmospheric duct within radar detection range is relatively perfect, but the long-distance inversion of atmospheric duct is li...
A hidden Markov model (HMM) is a useful tool for modeling dependent heterogeneous phenomena. It can be used to find factors that affect real-world events, even when those factors cannot be directly observed. HMMs differ from traditional methods by us...
Hidden Markov model (HMM) is a statistical signal prediction model, which has been widely used to predict economic regimes and stock prices. In this paper, we introduce the application of HMM in trading stocks (with S&P 500 index being an example...
In password guessing, the Markov model is still widely used due to its simple structure and fast inference speed. However, the Markov model based on random sampling to generate passwords has the problem of a high repetition rate, which leads to a low...
Conversion rule is a key element for a cellular automata (CA) model, and it is a significant and challenging issue for both domestic and international experts. Traditional research regarding CA models often constructs a single conversion rule for the...
In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hid...
Hidden Markov models are a very useful tool in the modeling of time series and any sequence of data. In particular, they have been successfully applied to the field of mathematical linguistics. In this paper, we apply a hidden Markov model to analyze...
As an emerging class of spatial trajectory data, mobile user trajectory data can be used to analyze individual or group behavioral characteristics, hobbies and interests. Besides, the information extracted from original trajectory data is widely used...
We analyzed Bitcoin’s cyclical patterns used by the Markov regime-switching model and explored the impacts of inflation and the US Dollar Index on Bitcoin’s cyclicality. The results showed Bitcoin’s cyclical pattern, the effects of...
This paper evaluates the prices of European-style options when dynamics of the underlying asset is assumed to follow a Markov-switching Heston’s stochastic volatility model. Under this framework, the expected return and the long-term mean of th...
21 December 2016
Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to bet...
With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to m...
Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order...
In order to control the development of urban space, it is important to explore scientific methods to provide a reference for regional territorial space planning. On the basis of the minimum cumulative resistance (MCR) model and the cellular automaton...
The map matching (MM) model plays an important role in revising the locations of floating car data (FCD) on a digital map. However, most existing MM models have multiple shortcomings, such as a low matching accuracy for complex roads, long running ti...
The hidden Markov model (HMM) is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial mar...
This paper develops a dynamic portfolio selection model incorporating economic uncertainty for business cycles. It is assumed that the financial market at each point in time is defined by a hidden Markov model, which is characterized by the overall e...
This paper presents a context-aware adaptive assembly assistance system meant to support factory workers by embedding predictive capabilities. The research is focused on the predictor which suggests the next assembly step. Hidden Markov models are an...
A Bayesian data analysis workflow offers great advantages to the process of measurement and verification, including the estimation of savings uncertainty regardless of the chosen numerical model. However, it is still rarely used in practice, perhaps...
The resource network is a non-linear threshold model where vertices exchange resource in infinite discrete time. The model is represented by a directed weighted graph. At each time step, all vertices send their resources along all output edges follow...
This study extracted the featured vectors in the same way from testing data and substituted these vectors into a trained hidden Markov model to get the log likelihood probability. The log likelihood probability was matched with the time–probabi...
To address the urgent need to accurately predict the spreading trend of the COVID-19 epidemic, a continuous Markov-chain model was, for the first time, developed in this work to predict the spread of COVID-19 infection. A probability matrix of infect...
The quantum model has been considered to be advantageous over the Markov model in explaining irrational behaviors (e.g., the disjunction effect) during decision making. Here, we reviewed and re-examined the ability of the quantum belief–action...
Determining states of the market and scientific laws of transfer between these states is an important subject in the field of financial mathematics. According to the results of market situation estimation, formulating corresponding trading strategies...
One of the key components of counterparty credit risk (CCR) measurement is generating scenarios for the evolution of the underlying risk factors, such as interest and exchange rates, equity and commodity prices, and credit spreads. Geometric Brownian...
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 im...
A compressive sensing-based target localization method based on hidden semi-Markov model (HsMM) is proposed to address problems like unpredictable data and the multipath effect of the Receive Signal Strength (RSS) in indoor localization. The method c...
Accurately predicting when calving will occur can provide great value in managing a dairy farm since it provides personnel with the ability to determine whether assistance is necessary. Not providing such assistance when necessary could prolong the c...
The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can...
The performance of urban bridges will deteriorate gradually throughout service life. Bridge deterioration prediction is essential for bridge management, especially for maintenance planning and decision-making. By considering the time-dependent reliab...
In the context of higher education, the wide availability of data gathered by universities for administrative purposes or for recording the evolution of students’ learning processes makes novel data mining techniques particularly useful to tack...
We propose an auto regressive integrated moving average Markov model (ARIMAMKM) for predicting annual energy consumption in China and enhancing the accuracy of energy consumption forecasts. This novel model extends the traditional auto regressive int...
This study showcased the Markov switching autoregressive model with time-varying parameters (MSAR-TVP) for modeling nonlinear time series with structural changes. This model enhances the MSAR framework by allowing dynamic parameter adjustments over t...
Traditional oil tank detection methods often use geometric shape information. However, it is difficult to guarantee accurate detection under a variety of disturbance factors, especially various colors, scale differences, and the shadows caused by vie...
Hidden Markov Model (HMM) is a well-known probabilistic framework for representing sequential phenomena governed by doubly stochastic processes. Specifically, it features a Markov chain with hidden (unobserved) states, where each state emits observab...
Bitcoin’s role in global finance has rapidly expanded with increasing institutional participation, prompting new questions about its linkage to macroeconomic variables. This study thoughtfully integrates a Bayesian Markov Chain Monte Carlo (MCM...
Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long...
Driving a vehicle, whether motorized or not, is a risky activity that can lead to a traffic accident and directly or indirectly affect all road users. In particular, road crashes involving pedestrians have caused the highest number of deaths and seri...
This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC) Simulation techniques. In this article, effort has been made to deve...
We detail the solution to the UCAmI Cup Challenge to recognizing on going activities at home from sensor measurements. We use binary sensors and proximity sensor measurements for the recognition. We use an hybrid strategy, combining a probabilistic m...
Understanding node mobility is critical for the proper simulation of mobile devices in a wireless network. However, current mobility models often do not reflect the realistic movements of users within their environments. They also do not provide the...
Advances in image processing technologies have provided more precise views in medical and health care management systems. Among many other topics, this paper focuses on several aspects of video-based monitoring systems for elderly people living indep...
Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher c...
In real life, individuals play an important role in the social networking system. When an epidemic breaks out the individual’s recovery rate depends heavily on the social network in which he or she lives. For this reason, in this paper a nonlin...
This study uses the hidden Markov model (HMM) to identify the phases of individual assets and proposes an investment strategy using price trends effectively. We conducted empirical analysis for 15 years from January 2004 to December 2018 on universes...
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