Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach

Loo, Becky P.Y.; Zhang, Feiyang; Hsiao, Janet H.; Chan, Antoni B.; Liu, Hui

doi:10.1007/s11769-021-1173-0

Cited by 9 publications

(3 citation statements)

References 52 publications

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“…Especially, we see that time series analysis methods are becoming more and more practical, and then some practical time series analysis software has appeared. Gradually, time series analysis methods and software have become impossible to predict and analyze those seemingly unrelated data [16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Application of Hidden Markov Model in Financial Time Series Data

Chang

2022

Security and Communication Networks

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Financial time series have typical characteristics such as outliers, trends, and mean reversion. The existence of outliers will affect the effectiveness of the unknown parameter estimation in the financial time series forecasting model, so that the forecasting error of the model will be larger. Quantitative forecasting methods are divided into causal forecasting method and time series forecasting method. The causal forecasting method uses the causal relationship between the predictor variable and other variables to predict, and the time series forecasting method infers the future value of the predictor variable based on the structure of the historical data of the predictor. Therefore, this paper proposes a hidden Markov model prediction method based on the observation vector sequence, which can simultaneously consider the influence of the variable sequence structure and related factors.

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Section: Introductionmentioning

confidence: 99%

Application of Hidden Markov Model in Financial Time Series Data

Chang

2022

Security and Communication Networks

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“…We have also used EMHMM to develop benchmarks for evaluating XAI methods (Yang, Folke, & Shafto, 2022). EMHMM has also been used in other fields including Behavioral Dentistry and Urban Mobility research (Loo, Zhang, Hsiao, Chan, & Lan, 2021; Zhang, Loo, Lan, Chan, & Hsiao, 2023). For example, in Behavioral Dentistry, by applying EMHMM to analyze children's eye movement behavior when viewing images with dental caries/injuries, we found that while poor dental aesthetics due to untreated caries or traumatic dental injuries are suggested to have adverse emotional and social impacts in adults, most preschool children did not show any attention bias toward or away from caries/injuries.…”

Section: Attention Strategymentioning

confidence: 99%

“…Also, most preschool children showed attention bias toward a midline diastema, whereas their educators averted their eyes, suggesting different interpretations between children and adults (Cho et al., 2022c). In Urban Mobility, through applying EMHMM to model and cluster travel patterns of working adults in Hong Kong, we discovered balanced versus work‐oriented lifestyle groups, with those living in the urban core demonstrating a more work‐oriented lifestyle (Loo et al., 2021; see also Zhang et al., 2023). We have developed an EMHMM Matlab toolbox, publicly available at http://visal.cs.cityu.edu.hk/research/emhmm/.…”

Section: Attention Strategymentioning

confidence: 99%

Understanding Human Cognition Through Computational Modeling

Hsiao

2024

Topics in Cognitive Science

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One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans’ belief on AI's operations and provide human‐centered explanations to enhance human−AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

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