scholarly journals Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator

2007 ◽  
Vol 2007 ◽  
pp. 1-13 ◽  
Author(s):  
Ronald Phlypo ◽  
Paul Boon ◽  
Yves D'Asseler ◽  
Ignace Lemahieu
Keyword(s):  
Time Domain ◽  
Lower Order ◽  
Statistical Models ◽  
Predictive Power ◽  
Simulated Data ◽  
Short Term ◽  
Ocular Movement ◽  
The Time Domain ◽  
Electroencephalogram Eeg

To cope with the severe masking of background cerebral activity in the electroencephalogram (EEG) by ocular movement artefacts, we present a method which combines lower-order, short-term and higher-order, long-term statistics. The joint smoothened subspace estimator (JSSE) calculates the joint information in both statistical models, subject to the constraint that the resulting estimated source should be sufficiently smooth in the time domain (i.e., has a large autocorrelation or self predictive power). It is shown that the JSSE is able to estimate a component from simulated data that is superior with respect to methodological artefact suppression to those of FastICA, SOBI, pSVD, or JADE/COM1 algorithms used for blind source separation (BSS). Interference and distortion suppression are of comparable order when compared with the above-mentioned methods. Results on patient data demonstrate that the method is able to suppress blinking and saccade artefacts in a fully automated way.

scholarly journals Recession Probabilities

2016 ◽  
pp. 1-6
Author(s):  
O. Emre Ergungor
Keyword(s):  
Simple Model ◽  
Lower Bound ◽  
Interest Rates ◽  
Statistical Models ◽  
Predictive Power ◽  
Yield Curve ◽  
Short Term ◽  
Term Spread ◽  
Portfolio Constraints

Statistical models that estimate 12-month-ahead recession probabilities using the term spread have been around for many years. However, the reliability of the term spread as a predictor may have been affected by short-term interest rates being at zero. At the zero lower bound, long-term yields cannot go too far into negative territory due to the portfolio constraints of institutional investors. Therefore, the yield curve may not invert when it should or as much as it should despite the anticipated path of the economy. I enhance the simple model with two variables that should have predictive power for recessions.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

Contrasting tactical and strategic dynamics on a Clausewitz landscape

2019 ◽  
Vol 17 (2) ◽  
pp. 143-153
Author(s):  
Rodrick Wallace
Keyword(s):  
Statistical Models ◽  
Limit Theorems ◽  
Large Scale ◽  
Evolutionary Process ◽  
Short Term ◽  
Local Resources ◽  
Human Organization ◽  
The World ◽  
Short Time

Statistical models based on the asymptotic limit theorems of control and information theories allow formal examination of the essential differences between short-time “tactical” confrontations and a long-term “strategic” conflict dominated by evolutionary process. The world of extended coevolutionary conflict is not the world of sequential “muddling through.” The existential strategic challenge is to take cognitive control of a long-term dynamic in which one may, in fact, be “losing” most short-term confrontations. Winning individual battles can be a relatively direct, if not simple or easy, matter of sufficient local resources, training, and resolve. Winning extended conflicts is not direct, and requires management of subtle coevolutionary phenomena subject to a dismaying punctuated equilibrium more familiar from evolutionary theory than military doctrine. Directed evolution has given us the agricultural base needed for large-scale human organization. Directed coevolution of the inevitable conflicts between the various segments of that organization may be needed for its long-term persistence.

Random Waves and Capsize Probability Based on Large Amplitude Motion Analysis

2002 ◽  
Author(s):  
Jan O. de Kat ◽  
Dirk-Jan Pinkster ◽  
Kevin A. McTaggart
Keyword(s):  
Time Domain ◽  
Random Waves ◽  
Extreme Wave ◽  
Small Probability ◽  
Safe Operation ◽  
Short Term ◽  
Physical Behavior ◽  
Wind Climate ◽  
Large Amplitude Motion

The objective of this paper is to apply a methodology aimed at the probabilistic capsize assessment of two naval ships: a frigate and a corvette. Use is made of combined knowledge of the wave and wind climate a ship will be exposed to during its lifetime and of the physical behavior of that ship in the various sea states it is likely to encounter. This includes the behavior in extreme wave conditions that have a small probability of occurrence, but which may be critical to the safe operation of a ship. Time domain simulations provide the basis for deriving short-term and long-term statistics for extreme roll angles. The numerical model is capable of predicting the 6 DOF behavior of a steered vessel in wind and waves, including conditions that may lead to broaching and capsizing.

scholarly journals Temporal Coding of Speech Information for Cochlear Implant Patients

1987 ◽  
Vol 96 (1_suppl) ◽  
pp. 62-64 ◽  
Author(s):  
J. B. Millar ◽  
L. F. A. Martin ◽  
Y. C. Tong ◽  
G. M. Clark
Keyword(s):  
Time Domain ◽  
Speech Processing ◽  
Excitation Frequency ◽  
Temporal Coding ◽  
Strongly Correlated ◽  
Initial Strategy ◽  
The Time Domain ◽  
Speech Information ◽  
Clinical Experiment

A modified speech-processing strategy incorporating the temporal coding of information strongly correlated with the first formant of speech was evaluated in a long-term clinical experiment with a single patient. The aim was to assess whether the patient could learn to extract information from the time domain in addition to the time domain cues for voice excitation frequency already received from the initial strategy. It was found that the patient gained no significant advantage from the modified strategy, but there was no disadvantage either, and the patient expressed a preference for the modified strategy for everyday use.

Long-Term Fatigue Analysis of Deepwater Risers in the Time Domain

2014 ◽  
Author(s):  
Yidan Gao ◽  
Ying Min Low
Keyword(s):  
Time Domain ◽  
Time Domain Analysis ◽  
Fatigue Analysis ◽  
Sea State ◽  
Need To Evaluate ◽  
Nature Of Time ◽  
The Time Domain ◽  
Computational Difficulty ◽  
Deepwater Risers

A floating production system is exposed to many different environmental conditions over its service life. Consequently, the long-term fatigue analysis of deepwater risers is computationally demanding due to the need to evaluate the fatigue damage from a multitude of sea states. Because of the nonlinearities in the system, the dynamic analysis is often performed in the time domain. This further compounds the computational difficulty owing to the time consuming nature of time domain analysis, as well as the need to simulate a sufficient duration for each sea state to minimize sampling variability. This paper presents a new and efficient simulation technique for long-term fatigue analysis. The results based on this new technique are compared against those obtained from the direct simulation of numerous sea states.

scholarly journals Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

2021 ◽  
pp. 135481662110584
Author(s):  
Ying Wang ◽  
Hongwei Zhang ◽  
Wang Gao ◽  
Cai Yang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Spillover Effects ◽  
Short Term ◽  
Time Frequency ◽  
Frequency Domains ◽  
Media Hype ◽  
The Time Domain ◽  
The Impact

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

scholarly journals The reliability of conditional Granger causality analysis in the time domain

2018 ◽  
Author(s):  
Raffaella Franciotti ◽  
Nicola Walter Falasca
Keyword(s):  
Time Domain ◽  
Simulated Data ◽  
Information Flows ◽  
Connection Strength ◽  
True Positive ◽  
Causality Analysis ◽  
Temporal Window ◽  
Time Points ◽  
Electrophysiological Signals ◽  
The Time Domain

Background. Brain function requires a coordinated flow of information among functionally specialized areas. Quantitative methods provide a multitude of metrics to quantify the oscillatory interactions measured by invasive or non-invasive recording techniques. Granger causality (G-causality) has emerged as a useful tool to investigate the directions of information flows, but challenges remain on the ability of G-causality when applying on biological data. In addition it is not clear if G-causality can distinguish between direct and indirect influences and if G-causality reliability was related to the strength of the neural interactions. Methods. In this study time domain G-causality connectivity analysis was performed on simulated electrophysiological signals. A network of 19 nodes was constructed with a designed structure of direct and indirect information flows among nodes, which we referred to as a ground truth structure. G-causality reliability was evaluated on two sets of simulated data while varying one of the following variables: the number of time points in the temporal window, the lags between causally interacting nodes, the connection strength between the links, and the noise. Results. Results showed that the number of time points in the temporal window affects G-causality reliability substantially. A large number of time points could decrease the reliability of the G-causality results, increasing the number of false positive (type I errors). In the presence of stationary signals, G-causality results are reliable showing all true positive links (absence of type II errors), when the underlying structure has the delays between the interacting nodes lower than 100 ms, the connection strength higher to 0.1 time the amplitude of the driver signal and good signal to noise ratio. Finally, indirect links were revealed by G-causality analysis for connection strength higher than the direct link and lags lower than the direct link. Discussion. Conditional multivariate vector autoregressive model was applied to 19 virtual time series to estimate the reliability of the G-causality analysis on the identification of the true positive link, on the presence of spurious links and on the effects of indirect links. Simulated data revealed that weak direct but not weak indirect causal effects could be identified by G-causality analysis. These results demonstrate a good sensitivity and specificity of the conditional G-causality analysis in the time domain when applied on covariance stationary, non-correlated electrophysiological signals.

scholarly journals Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes

2021 ◽  
Vol 8 (9) ◽  
pp. 202245
Author(s):  
Ren-Meng Cao ◽  
Xiao Fan Liu ◽  
Xiao-Ke Xu
Keyword(s):  
Information Diffusion ◽  
Predictive Power ◽  
Diffusion Processes ◽  
Temporal Dynamics ◽  
Learning Algorithm ◽  
Short Term ◽  
Information Cascade ◽  
Term Prediction ◽  
Diffusion Dynamics

Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management and infodemic controlling. However, predicting the scale of an information cascade in the long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the user following network, retweeting network, tweet content and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension of the information diffusion process and may assist in the control of such a process.

scholarly journals Multiple Spatiotemporal Characteristics-Based Zonal Voltage Control for High Penetrated PVs in Active Distribution Networks

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 249 ◽  
Author(s):  
Chuanliang Xiao ◽  
Lei Sun ◽  
Ming Ding
Keyword(s):  
Time Domain ◽  
Reactive Power ◽  
Voltage Control ◽  
Distribution Networks ◽  
Spatial Domain ◽  
Active Power ◽  
Short Term ◽  
Control Scheme ◽  
The Time Domain ◽  
Active Distribution Networks

The penetration of photovoltaic (PV) outputs brings great challenges to optimal operation of active distribution networks (ADNs), especially leading to more serious overvoltage problems. This study proposes a zonal voltage control scheme based on multiple spatiotemporal characteristics for highly penetrated PVs in ADNs. In the spatial domain, a community detection algorithm using a reactive/ active power quality function was introduced to partition an ADN into sub-networks. In the time domain, short-term zonal scheduling (SZS) with 1 h granularity was drawn up based on a cluster. The objective was to minimize the supported reactive power and the curtailed active power in reactive and active power sub-networks. Additionally, a real-time zonal voltage control scheme (RZVC) with 1 min granularity was proposed to correct the SZS rapidly by choosing and controlling the key PV inverter to regulate the supported reactive power and the curtailed active power of the inverters to prevent the overvoltage in each sub-network. With the time domain cooperation, the proposed method could achieve economic control and avoid overvoltage caused by errors in the forecast data of the PVs. For the spatial domain, zonal scheduling and zonal voltage control were carried out in each cluster, and the short-term scheduling and voltage controlling problem of the ADN could then be decomposed into several sub-problems. This could simplify the optimization and control which can reduce the computing time. Finally, an actual 10kV, 103-node network in Zhejiang Province of China is employed to verify the effectiveness and feasibility of the proposed approach.

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