scholarly journals Time Window Determination for Inference of Time-Varying Dynamics: Application to Cardiorespiratory Interaction

2020 ◽  
Vol 11 ◽  
Author(s):  
Dushko Lukarski ◽  
Margarita Ginovska ◽  
Hristina Spasevska ◽  
Tomislav Stankovski
Keyword(s):  
Time Window ◽  
Time Varying ◽  
Cardiorespiratory Interaction

scholarly journals Oil Price Forecasting Using a Time-Varying Approach

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1403
Author(s):  
Lu-Tao Zhao ◽  
Shun-Gang Wang ◽  
Zhi-Gang Zhang
Keyword(s):  
Crude Oil ◽  
Time Window ◽  
Oil Prices ◽  
Oil Price ◽  
Price Forecasting ◽  
Time Varying ◽  
Crude Oil Price ◽  
Trend Prediction ◽  
Oil Market ◽  
Polynomial Decomposition

The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

scholarly journals Distribution Route Optimization for Electric Vehicles in Urban Cold Chain Logistics for Fresh Products under Time-Varying Traffic Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Zhixue Zhao ◽  
Xiamiao Li ◽  
Xiancheng Zhou
Keyword(s):  
Electric Vehicles ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Route Optimization ◽  
Cold Chain ◽  
Logistic Distribution ◽  
Time Varying ◽  
Traffic Conditions ◽  
Distribution Process ◽  
Fresh Products

Electric vehicles (EVs) have been widely used in urban cold chain logistic distribution and transportation of fresh products. In this paper, an electric vehicle routing problem (EVRP) model under time-varying traffic conditions is designed for planning the itinerary for fresh products in the urban cold chain. The object of the EVRP model is to minimize the total cost of logistic distribution that includes economic cost and fresh value loss cost. To reflect the real situation, the EVRP model considers several influencing factors, including time-varying road network traffic, road type, client’s time-window requirement, freshness of fresh products, and en route queuing for charging. Furthermore, to address the EVRP, an improved adaptive ant colony algorithm is designed. Simulation test results show that the proposed method can allow EVs to effectively avoid traffic congestion during the distribution process, reduce the total distribution cost, and improve the performance of the cold chain logistic distribution process for fresh products.

Global exponential stability of memristive neural networks with impulse time window and time-varying delays

2016 ◽  
Vol 171 ◽  
pp. 1021-1026 ◽  
Author(s):  
Degang Yang ◽  
Guoying Qiu ◽  
Chuandong Li
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Time Window ◽  
Time Varying ◽  
Memristive Neural Networks

Integrating Time-Varying and Ecological Exposures into Multivariate Analyses of Hospital-Acquired Infection Risk Factors: A Review and Demonstration

2016 ◽  
Vol 37 (4) ◽  
pp. 411-419 ◽  
Author(s):  
Kevin A. Brown ◽  
Nick Daneman ◽  
Vanessa W. Stevens ◽  
Yue Zhang ◽  
Tom H. Greene ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Statistical Methods ◽  
Time Window ◽  
Ecological Factors ◽  
Time Varying ◽  
Multivariate Statistical ◽  
Individual Level ◽  
Infection Risk Factors ◽  
Hospital Acquired

OBJECTIVESHospital-acquired infections (HAIs) develop rapidly after brief and transient exposures, and ecological exposures are central to their etiology. However, many studies of HAIs risk do not correctly account for the timing of outcomes relative to exposures, and they ignore ecological factors. We aimed to describe statistical practice in the most cited HAI literature as it relates to these issues, and to demonstrate how to implement models that can be used to account for them.METHODSWe conducted a literature search to identify 8 frequently cited articles having primary outcomes that were incident HAIs, were based on individual-level data, and used multivariate statistical methods. Next, using an inpatient cohort of incident Clostridium difficile infection (CDI), we compared 3 valid strategies for assessing risk factors for incident infection: a cohort study with time-fixed exposures, a cohort study with time-varying exposures, and a case-control study with time-varying exposures.RESULTSOf the 8 studies identified in the literature scan, 3 did not adjust for time-at-risk, 6 did not assess the timing of exposures in a time-window prior to outcome ascertainment, 6 did not include ecological covariates, and 6 did not account for the clustering of outcomes in time and space. Our 3 modeling strategies yielded similar risk-factor estimates for CDI risk.CONCLUSIONSSeveral common statistical methods can be used to augment standard regression methods to improve the identification of HAI risk factors.Infect. Control Hosp. Epidemiol. 2016;37(4):411–419

scholarly journals A Four-Dimensional Incremental Analysis Update for the Ensemble Kalman Filter

2016 ◽  
Vol 144 (7) ◽  
pp. 2605-2621 ◽  
Author(s):  
Lili Lei ◽  
Jeffrey S. Whitaker
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Time Window ◽  
Three Dimensional ◽  
Equation Model ◽  
Time Varying ◽  
State Variables ◽  
Incremental Analysis ◽  
Real Atmosphere ◽  
Better Than

Abstract The analysis produced by the ensemble Kalman filter (EnKF) may be dynamically inconsistent and contain unbalanced gravity waves that are absent in the real atmosphere. These imbalances can be exacerbated by covariance localization and inflation. One strategy to combat the imbalance in the analyses is the incremental analysis update (IAU), which uses the dynamic model to distribute the analyses increments over a time window. The IAU has been widely used in atmospheric and oceanic applications. However, the analysis increment that is gradually introduced during a model integration is often computed once and assumed to be constant for an assimilation window, which can be seen as a three-dimensional IAU (3DIAU). Thus, the propagation of the analysis increment in the assimilation window is neglected, yet this propagation may be important, especially for moving weather systems. To take into account the propagation of the analysis increment during an assimilation window, a four-dimensional IAU (4DIAU) used with the EnKF is presented. It constructs time-varying analysis increments by applying all observations in an assimilation window to state variables at different times during the assimilation window. It then gradually applies these time-varying analysis increments through the assimilation window. Results from a dry two-layer primitive equation model and the NCEP GFS show that EnKF with 4DIAU (EnKF-4DIAU) and 3DIAU (EnKF-3DIAU) reduce imbalances in the analysis compared to EnKF without initialization (EnKF-RAW). EnKF-4DIAU retains the time-varying information in the analysis increments better than EnKF-3DIAU, and produces better analysis and forecast than either EnKF-RAW or EnKF-3DIAU.

scholarly journals Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Yajie Qi ◽  
Huajiao Li ◽  
Sui Guo ◽  
Sida Feng
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Time Window ◽  
Energy Industry ◽  
Time Varying ◽  
Transmission Characteristics ◽  
Investor Attention ◽  
Correlation Relationship ◽  
Transmission Modes ◽  
The Relationship

The relationship between investor attention and stock prices has been a topic of interest in economics. Previous studies have shown that the correlation relationship between the two changes with time. However, there are few studies to explore the time-varying evolution of the relationship, as well as the transmission characteristics under important cycles. Thus, this paper is dedicated to discover the dynamic transmission characteristics of the correlation between investor attention and stock price. We selected the typical stocks of China’s energy industry, PetroChina and Sinopec, as the research objects, as they occupy a large market share and are representative. And the transaction data and attention data are used to build investor attention indicator. In order to reproduce the dynamic transmission process of correlation at different cycles, sliding time window and complex network are applied. The results show that PetroChina and Sinopec stocks have a weakly negative correlation between investor attention and stock price from 2017 to 2018. However, from the perspective of different cycles, the correlation has time-varying characteristics. As the cycle grows, the types of transmission patterns of the five consecutive days of correlation between the two become less, but the transmission intensity between the modes increases and the transition becomes more regular and inclined. In addition, by mining the important transmission modes and main transmission paths under important periods, we find that the series modes of uncorrelated or weakly positive correlation for five consecutive days dominate the transition of modes in the networks. Also, the closed loop formed by these two important modes and related modes is the main transmission path. These findings can reveal the rules of the typical stock market in China’s energy industry and help investors with different investment cycle preferences make sound decisions.

scholarly journals Exponential Stability of Cohen-Grossberg Neural Networks with Impulse Time Window

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Mei Liu ◽  
Haijun Jiang ◽  
Cheng Hu
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Numerical Simulations ◽  
Lyapunov Functional ◽  
Time Window ◽  
Time Varying ◽  
Stability Criteria ◽  
Analysis Method ◽  
Functional Theory ◽  
Inequality Technique

This paper concerns the problem of exponential stability for a class of Cohen-Grossberg neural networks with impulse time window and time-varying delays. In our letter, the impulsive effects we considered can stochastically occur at a definitive time window and the impulsive controllers we considered can be nonlinear and even rely on the states of all the neurons. Hence, the impulses here can be more applicable and more general. By utilizing Lyapunov functional theory, inequality technique, and the analysis method, we obtain some novel and effective exponential stability criteria for the Cohen-Grossberg neural networks. These results generalize a few previous known results and numerical simulations are given to show the effectiveness of the derived results.

An online identification approach for a nonlinear ship motion model based on a receding horizon

2021 ◽  
pp. 014233122110196
Author(s):  
Jian Zheng ◽  
Ming Yan ◽  
Yun Li ◽  
Changhai Huang ◽  
Yiping Ma ◽  
...  
Keyword(s):  
Real Time ◽  
Time Window ◽  
Control Object ◽  
Ship Motion ◽  
Identification Algorithm ◽  
Time Varying ◽  
Ocean Engineering ◽  
Online Identification ◽  
Motion System ◽  
Window Method

The ship motion system is a nonlinear control object, and its system parameters exhibit time-varying characteristics with the ship motion state, which increases the difficulty of control. Therefore, parameter identification has an important significance for the stability of ship motion control. Aiming at the real-time identification problem of the nonlinear and time-varying ship motion system during movement, this paper reconstructs the ship motion system with the propeller speed and rudder angle as control variables and designs an online identification algorithm with the sliding time window method based on the extended Kalman filter algorithm. In addition, to solve the problem of noise in ship motion data collected in real-time, a real-time wavelet filter is developed to perform online preprocessing of the input data of the identification algorithm. The applicability of the method is further demonstrated via a model-scale Korea Research Institute of Ships and Ocean Engineering container ship free-running experiments in a basin.

Phase-only correlation of time-varying spectral representations of microseismic data for identification of similar seismic events

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. WC37-WC45 ◽  
Author(s):  
Hirokazu Moriya
Keyword(s):  
Correlation Function ◽  
Time Window ◽  
Signal To Noise Ratio ◽  
Image Data ◽  
Seismic Events ◽  
Time Varying ◽  
Time Frequency ◽  
Seismic Image ◽  
Wide Range ◽  
Spectral Representations

Identification of similar seismic events is important for precise estimation of source locations and for evaluation of subsurface structure. Phase-only correlation is well known as a real-time image-matching method for fingerprint identification. I applied the phase-only correlation in a geophysical context to identify similar waveforms among microseismic events. The waveforms were first transformed into time-varying spectral representations to express frequency content in the time-frequency domain. The phase-only correlation function is calculated between two time-varying spectral representations and similarity is evaluated using the peak value of the phase-only correlation function. This method was applied to arbitrarily selected waveforms from aftershocks of an earthquake in Japan to assess its ability to identify similar waveforms perturbed by white noise. The detection of similarity of the proposed algorithm was compared to the similarity as detected by a 2D crosscorrelation function of the time-varying spectral representation and a 1D crosscorrelation of the raw waveform. This showed that the phase-only correlation function exhibits a sharp peak that quantifies similarity and dissimilarity over a wide range of signal-to-noise ratio (S/N) and remained unaffected by the length of the time window used to estimate time-varying spectral representations. Phase-only correlation may also have applications in other geophysical analyses and interpretations that are based on waveform and seismic image data.

Sign in / Sign up

Export Citation Format

Share Document