I watch SEM: continuous time dynamic models withN≥1 smart watch data

Automated guided vehicles (AGVs) in the U-shaped automated container terminal travel longer and more complex paths. The conflicts among AGVs are trickier. The scheduling strategy of the traditional automated container terminal is difficult to be applied to the U-shaped automated container terminal. In order to minimize the handling time of all tasks and avoid AGV conflicts simultaneously in the U-shaped automated container terminal, this paper establishes a hybrid programming model for conflict-free integrated scheduling of quay cranes, AGVs, and double-cantilever rail cranes in the unloading process. It consists of a discrete event dynamic model and a continuous time dynamic model. An improved genetic seagull optimization algorithm (GSOA) is designed. A series of numerical experiments are conducted to verify the effectiveness and the efficiency of the model and the algorithm. The results show that the proposed method can simultaneously realize the AGVs collision avoidance and multi-equipment integrated scheduling optimization in the U-shaped automated container terminal.

Many important factors affect the spread of childhood infectious disease. To better understand the fundamental drivers of infectious disease spread, several researchers have estimated seasonal transmission coefficients in discrete-time models. In this paper, we build upon this previous work and also develop a framework for efficient estimation using continuous differential equation models. We introduce nonlinear programming formulations to efficiently estimate model parameters and seasonal transmission profiles from existing case count data for the childhood disease, measles. We compare results from discrete time and continuous time models and address several shortcomings of the discrete-time method, including removing the need for the data reporting interval to match the time between successive cases in the chain of transmission or serial interval of the disease. Using a simultaneous approach for optimization of differential equation systems, we demonstrate that seasonal transmission parameters can be effectively estimated using continuous time models instead of discrete-time models.

Chapter 34 - Hierarchical continuous time modeling

American Option Pricing with Discrete and Continuous Time Models: An Empirical Comparison

A Presentation of the Italian Continuous Time Model

In this article, we provide empirical evidence of the recent financial crisis over 2007–2009 using discrete time multivariate GARCH (MGARCH) models and continuous time modelling approaches. Using daily data for 14 countries, we investigate the return and volatility spillovers among the US and other international markets. The MGARCH results reveal positive return spillovers from the US to a number of markets, and volatility transmission is verified. The US market is prone to return and volatility transmission from a limited number of markets. The continuous time analysis finds evidence of feedback effects in some cases. Evidence shows that spillover effects intensified during the financial crisis.

In this chapter we review continuous time series modeling and estimation by extended structural equation models (SEM) for single subjects and N > 1. First-order as well as higher-order models will be dealt with. Both will be handled by the general state space approach which reformulates higher-order models as first-order models. In addition to the basic model, the extensions of exogenous variables and traits (random intercepts) will be introduced. The connection between continuous time and discrete time for estimating the model by SEM will be made by the exact discrete model (EDM). It is by the EDM that the exact estimation procedure in this chapter differentiates from many approximate procedures found in the literature. The proposed analysis procedure will be applied to the well-known Wolfer sunspot data, an N = 1 time series that has been analyzed by several continuous time analysts in the past. The analysis will be carried out by ctsem, an R-package for continuous time modeling that interfaces to OpenMx, and the results will be compared to those reported in the previous studies.

Activity recognition is mainly used in health care, authentication and many other fields. Activity recognition based on a smart watch usually performs much better than any other means of activity recognition in these areas. With its small size, high integration and multi-function, smart watch holds more advantages over the devices used in image based activity recognition, for example, a monitor, which is usually location fixed and view limited. In this paper, we collected data of five behaviors, i.e. stand, walk, run, upstairs, downstairs from the accelerometer on a smart watch. These data are classified with CART decision tree. Experimental results prove that the proposed method improves the accuracy of activity recognition.

Solid State Transformer (SST) is widely used in distribution renewable energy resources. SST includes AC-DC rectifier, DC-DC Dual Active Bridge (DAB) converter and AC-DC inverter. In this paper, continuous time average model and dynamic phasor model for combined Rectifier and DAB converter (R-DAB) are developed. Control scheme for R-DAB model is proposed for dynamic phasor based model, in which rectifier and DAB are being individually controlled using PI controller. The effect of controller has been analysed with change in input grid voltage variation. Stability analysis has performed using small signal stability analysis and Lyapunov's criterion. Developed model is verified with simulation in MATLAB/Simulink. Simulation result verifies the proposed modelling technique and controller performance.

Limitations record on a variable which have continuous variable changes make the 'time' attached to the data is discrete. That underlies development of continuous time model. Compared to discrete time model, the continuous time model has advantages that can make prediction at different lag. In addition, it still can perform continuous time model parameter estimation, although the objects observations do not always have the same time interval (irregular time). Thus, the purpose of this study is to examine the consistency of continuous time model coefficient parameter with structural equation model (SEM) approach in irregular time data. The method used is the Exact Discrete Model (EDM) with SEM approach. EDM is a model that connects the parameter to value which is underlying parameter of the continuous time model with non-linear relationships. The result analysis of the research indicated that the coefficients of continuous time model parameter are still consistent until 20% of irregular time with 5% consistency limits.

A sequential refinement approach for parameter optimization in continuous dynamic models

R. Garcia-Millan etal. [Phys. Rev. E 91, 042122 (2015)PLEEE81539-375510.1103/PhysRevE.91.042122] reported a universal finite-size scaling form of the survival probability in discrete time branching processes. In this comment, we generalize the argument to a wide range of continuous time branching processes. Owing to the continuity, the resulting differential (rather than difference) equations can be solved in closed form, rendering some approximations by R. Garcia-Millan etal. superfluous, although we work along similar lines. In the case of binary branching, our results are in fact exact. Demonstrating that discrete time and continuous time models have their leading order asymptotics in common, raises the question to what extent corrections are identical.

Chapter 47 is a brief outline of a paper presented at a meeting at Nuffield College, Oxford in 1962. It refers to chapter 48 for details of proposed methods of estimating the parameters of continuous time dynamic models from observations of the variables at equispaced points of time. Chapter 48 is a sequel to chapter 42, an important and influential essay which Phillips had published in Biometrika in 1959.

This paper describes a case study of event and continuous hydrologic modeling in the Kelani River basin in Sri Lanka using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC–HMS). An extremely high rainfall event in November 2005 was used to calibrate model parameters, and extremely high rainfall events in April–May 2008, May–June 2008, and May 2010 were used to validate the event model. The calibrated, direct runoff and base flow parameters were then used in the continuous hydrologic model. The Green and Ampt infiltration loss method was used to account for infiltration loss in event-based modeling and a five-layer soil moisture accounting loss method was employed in continuous modeling. The Clark unit hydrograph method and the recession base flow method were used to simulate direct runoff and base flow, respectively. The results depict the capability of HEC–HMS to reproduce streamflows in the basin to a high accuracy with averaged computed Nash–Sutcliffe efficiencies of 0.91 for e...

Intensive longitudinal studies, such as ecological momentary assessment studies using electronic diaries, are gaining popularity across many areas of psychology. Multilevel models (MLMs) are most widely used analytical tools for intensive longitudinal data (ILD). Although ILD often have individually distinct patterns of serial correlation of measures over time, inferences of the fixed effects, and random components in MLMs are made under the assumption that all variance and autocovariance components are homogenous across individuals. In the present study, we introduced a multilevel model with Cholesky transformation to model ILD with individually heterogeneous covariance structure. In addition, the performance of the transformation method and the effects of misspecification of heterogeneous covariance structure were investigated through a Monte Carlo simulation. We found that, if individually heterogeneous covariances are incorrectly assumed as homogenous independent or homogenous autoregressive, MLMs produce highly biased estimates of the variance of random intercepts and the standard errors of the fixed intercept and the fixed effect of a level 2 covariate when the average autocorrelation is high. For intensive longitudinal data with individual specific residual covariance, the suggested transformation method showed lower bias in those estimates than the misspecified models when the number of repeated observations within individuals is 50 or more.