We propose a confirmatory dynamic factor model for a large number of stocks whose returns are observed daily across multiple time zones. The model has a global factor and a continental factor that both drive the indiv...
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Value-based reinforcement-learning algorithms have shown strong results in games, robotics, and other real-world applications. Overestimation bias is a known threat to those algorithms and can sometimes lead to dramat...
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This paper develops a novel method for policy choice in a dynamic setting where the available data is a multi-variate time series. Building on the statistical treatment choice framework, we propose Time-series Empiric...
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We present an approach for parameter estimation with multirate measurements, with the slow measurements having variable time delays due to laboratory analysis, and also being functions of all the states during the sam...
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We present an approach for parameter estimation with multirate measurements, with the slow measurements having variable time delays due to laboratory analysis, and also being functions of all the states during the sample collection. We formulate a particle filter-based approach under the framework of the expectation maximization algorithm to develop the estimates. The effectiveness and applicability of the proposed method are demonstrated though a simulation example, a hybrid tank experiment and an industrial case study;in each case, the slow and fast measurements are for the same variable. We show that this approach results in improved parameter estimation when the information from the delayed measurements is fused with the fast measurement information.
With the continuous improvement of the complexity and comprehensive level of the system, its reliability becomes more and more important. The remaining useful life (RUL) estimation method using the degradation model w...
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With the continuous improvement of the complexity and comprehensive level of the system, its reliability becomes more and more important. The remaining useful life (RUL) estimation method using the degradation model with random effect to describe the degradation process of the system has been widely used such as Wiener process. However, the conventional Wiener-process-based degradation model only considers the current monitoring data but not the historical degradation data, which leads to the inaccuracy of RUL prediction. Furthermore, in engineering, there will always be data missing caused by sensor networks, long life cycle properties of system and so on, leading to unsatisfactory results. This paper contributed a RUL re-prediction method based on Wiener process combining the current monitoring status and historical degradation data of the system. In the initial prediction process, the Wiener process is used to describe the degradation process of the system, the drift coefficient and diffusion coefficient are estimated by expectation maximization algorithm (EM algorithm), and the dynamic Bayesian networks (DBNs) model for system performance degradation is established to solve the uncertainty caused by missing data. In the re-prediction process, n groups of performance degradation monitoring data and historical predicted data are combined to calculate the basic degradation in each stage of Wiener process, and the DBNs are used for modeling. The RUL value is obtained by the time difference between the detection point and the predicted fault point, it is determined by the failure threshold finally. A case of subsea Christmas tree system is adopted to demonstrate the proposed approach.
The stock market index forecasting is quite a popular topic in the present economy. There are many micro and macro-economic factors which influence the stock prices. With the emergence of machine learning techniques, ...
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ISBN:
(纸本)9781728151977;9781728151960
The stock market index forecasting is quite a popular topic in the present economy. There are many micro and macro-economic factors which influence the stock prices. With the emergence of machine learning techniques, algorithmic trading became most popular in forecasting the stock prices. There are many traditional machine learning techniques like ARMA, ARIMA which are used to forecast the stock market index. However these techniques consider trends and patterns involved in the data. Since the stock market data is a time series and irregular in nature there will be some noise involved. The present research paper studies the forecasting of stock market index by applying Hidden Markov models which considers the hidden states within the stock market index and traditional ARIMA model. 11MM considers the posterior probabilities on different hidden states using expectation maximization algorithm. The data considered for the study includes 5 different stock market index Dow Jones, NIFTY 50, 5 & P 500, New York Stock Index (NYSE) and KOSPL The data includes daily prices of Low, High, Close, Open, Volume for a period of 5 years that is 2014-2019 which accounts to approximately 1380 data points. In 11MM model the closing price of the index is considered for determining the transition states and posterior probabilities at 2, 3, 4 and 5 hidden states. Akaike Information Criteria (AIC) and Bayesian information Criterion (BIC) are used to determine the states from HMM. The research paper studies the direction of the market the closing price of the stock index considering HMM. The paper is divided into 5 parts which include Part 1: Introduction, Part 2: Past study, Part 3: Machine Learning algorithms, Part4: Data Analysis using HMM and Part 5: Results and Conclusion.
We present a noise-injected version of the expectation-maximization (EM) algorithm: the noisy expectation-maximization (NEM) algorithm. The NEM algorithm uses noise to speed up the convergence of the EM algorithm. The...
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We present a noise-injected version of the expectation-maximization (EM) algorithm: the noisy expectation-maximization (NEM) algorithm. The NEM algorithm uses noise to speed up the convergence of the EM algorithm. The NEM theorem shows that additive noise speeds up the average convergence of the EM algorithm to a local maximum of the likelihood surface if a positivity condition holds. Corollary results give special cases when noise improves the EM algorithm. We demonstrate these noise benefits on EM algorithms for three data models: the Gaussian mixture model (GMM), the Cauchy mixture model (CMM), and the censored log-convex gamma model. The NEM positivity condition simplifies to a quadratic inequality in the GMM and CMM cases. A final theorem shows that the noise benefit for independent identically distributed additive noise decreases with sample size in mixture models. This theorem implies that the noise benefit is most pronounced if the data is sparse.
Single particle cryo-EM excels in determining static structures of protein molecules, but existing 3D reconstruction methods have been ineffective in modelling flexible proteins. We introduce 3D variability analysis (...
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Single particle cryo-EM excels in determining static structures of protein molecules, but existing 3D reconstruction methods have been ineffective in modelling flexible proteins. We introduce 3D variability analysis (3DVA), an algorithm that fits a linear subspace model of conformational change to cryo-EM data at high resolution. 3DVA enables the resolution and visualization of detailed molecular motions of both large and small proteins, revealing new biological insight from single particle cryo-EM data. Experimental results demonstrate the ability of 3DVA to resolve multiple flexible motions of a-helices in the sub-50 kDa transmembrane domain of a GPCR complex, bending modes of a sodium ion channel, five types of symmetric and symmetry-breaking flexibility in a proteasome, large motions in a spliceosome complex, and discrete conformational states of a ribosome assembly. 3DVA is implemented in the cryoSPARC software package.
Identifying key influencers from time series data without a known prior network structure is a challenging problem in various applications, from crime analysis to social media. While much work has focused on event-bas...
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This paper develops a general framework for dynamic models in which individuals simultaneously make both discrete and continuous choices. The framework incorporates a wide range of unobserved heterogeneity. I show tha...
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