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Hybrid Adaptive Parametric Frequency Analysis

Kriton Konstantinidis, Emery Brown
2020 EAI Endorsed Transactions on Bioengineering and Bioinformatics  
Conclusion: The suitability of the proposed method for online use, in combination with its ability to smoothly track frequency changes in human EEG signals under the most common anesthetics, suggests that  ...  Such online use can facilitate the design of more precise closed loop systems for automatic control of brain states under general anesthesia.  ...  Model selection was performed in an initial burn-in period of the anesthesia procedure (10 seconds for the experiments presented).  ... 
doi:10.4108/eai.8-7-2020.165514 fatcat:qpo54k3a4ndldkgwzabaqtwvhi

Stopping time detection of wood panel compression: A functional time series approach [article]

H. L. Shang, J. Cao, P. Sang
2022 arXiv   pre-print
We consider determining the optimal stopping time for the glue curing of wood panels in an automatic process environment.  ...  Using the near-infrared spectroscopy technology to monitor the manufacturing process ensures substantial savings in energy and time.  ...  of a non-overlapping block bootstrap procedure.  ... 
arXiv:2204.13197v1 fatcat:3u3ist6ozvbq5jve7h7kak5chy

Sieve bootstrap monitoring persistence change in long memory process

Zhanshou Chen, Zheng Tian, Yuhong Xing
2016 Statistics and its Interface  
We show that the proposed monitoring scheme is consistent for stationary to nonstationary change. In particular, a sieve bootstrap approximation method is proposed.  ...  Simulations indicate that the new monitoring procedure performs well in finite samples. Finally, we illustrate our monitoring procedure using a set of foreign exchange rate data.  ...  Although Kirch (2008) [22] has argued that bootstrap method performs well to sequentially detect change point in online data, there are few papers that concentrate on sieve bootstrap for online long  ... 
doi:10.4310/sii.2016.v9.n1.a4 fatcat:3en2gv72wvckrkhne4oxfr2zra

A short note on quantifying and visualizing yearly variation in online monitored temperature data

G. Kauermann, T. Mestekemper
2012 Statistical Modelling  
Figure 4 Course of the time-warping functionsπ i (d) for the six years of our dataset (black solid lines).  ...  The grey band marks the area where all bootstrapped time-warping functionsπ * i (d) where contained in. Observed landmarks are shown visualized by circles  ...  We want, however, a procedure reacting 'online' by looking at recent measurements.  ... 
doi:10.1177/1471082x1001200204 fatcat:cycdkfpw2fbxjplz6ntkleoioy

Monitoring Volatility Change for Time Series Based on Support Vector Regression

Sangyeol Lee, Chang Kyeom Kim, Dongwuk Kim
2020 Entropy  
The proposed online monitoring process is designed to detect a significant change in volatility of financial time series.  ...  A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and the stock price of Microsoft Corporation is presented to demonstrate the versatility of our model.  ...  monitoring process in autoregressive time series.  ... 
doi:10.3390/e22111312 pmid:33287077 fatcat:kcdowmn75rdhbn7xbxs7o34mhq

Time Series Analysis Application on Industry 4.0: PreCoM Project

Manuel Vaamonde-Rivas, Manuel Febrero-Bande, Beatriz Pateiro-López
2021 Zenodo  
In this paper we present a new Time Series Analysis software (TSA) module for the evaluation of the condition of machine components.  ...  In both cases the strategy consists on fitting a predictive time series model to a variable that indicates the condition of a target component.  ...  The goal of the online part is to provide condition based monitoring in real time.  ... 
doi:10.5281/zenodo.4556819 fatcat:nsroekxdjreqtbzxi3sczxo4x4

Quantifying uncertainty on sediment loads using bootstrap confidence intervals

Johanna I. F. Slaets, Hans-Peter Piepho, Petra Schmitter, Thomas Hilger, Georg Cadisch
2017 Hydrology and Earth System Sciences  
The method was tested for a small watershed in Northwest Vietnam for the period 2010–2011.  ...  A bootstrap method is then developed that accounts for the uncertainty in the concentration and discharge predictions, allowing temporal correlation in the constituent data, and can be used when data transformations  ...  ., doi:10.5194/hess-2016-264, 2016 Manuscript under review for journal Hydrol. Earth Syst. Sci. Published: 21 June 2016 c Author(s) 2016. CC-BY 3.0 License.  ... 
doi:10.5194/hess-21-571-2017 fatcat:tyyy5mn2fzhabg3ckdgxnmmffu

Detection and classification of subject-generated artifacts in EEG signals using autoregressive models

Vernon Lawhern, W. David Hairston, Kaleb McDowell, Marissa Westerfield, Kay Robbins
2012 Journal of Neuroscience Methods  
We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts.  ...  Modeling of artifacts is performed using autoregressive (AR) modeling of artifact-contaminated EEG signals.  ...  The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory  ... 
doi:10.1016/j.jneumeth.2012.05.017 pmid:22634706 fatcat:bqwteiukwvhonie5xurep6qp64

A statistical analysis of time trends in atmospheric ethane

Marina Friedrich, Eric Beutner, Hanno Reuvers, Stephan Smeekes, Jean-Pierre Urbain, Whitney Bader, Bruno Franco, Bernard Lejeune, Emmanuel Mahieu
2020 Climatic Change  
In particular, for the broken trend model, we propose a bootstrap method for inference on the break location and the corresponding changes in slope.  ...  Our autoregressive wild bootstrap approach, combined with a seasonal filter, is able to handle all issues mentioned above (we provide R code for all proposed methods on https://www.stephansmeekes.nl/code  ...  For the construction of confidence intervals in the nonparametric model, we use an autoregressive wild bootstrap method.  ... 
doi:10.1007/s10584-020-02806-2 fatcat:aah26z2ndfboth42kfvvzfj474

Quantifying uncertainty on sediment loads using bootstrap confidence intervals

Johanna I. F. Slaets, Hans-Peter Piepho, Petra Schmitter, Thomas Hilger, Georg Cadisch
2016 Hydrology and Earth System Sciences Discussions  
A bootstrap method is then developed that accounts for the uncertainty in the concentration and discharge predictions, allowing temporal correlation in the constituent data, and can be used when data transformations  ...  The method was tested for a small watershed in Northwest Vietnam for the period 2010–2011.  ...  The fieldwork data in this study were collected within the framework of the Uplands Program collaborative research center, a DFG-funded project in collaboration with Tran Duc Vien at the Hanoi University  ... 
doi:10.5194/hess-2016-264 fatcat:mwiynp2yxfcxpa6gebmulut6mm

Exploring the role of non-pharmaceutical interventions (NPIs) in flattening the Greek COVID-19 epidemic curve

Amaryllis Mavragani, Konstantinos Gkillas
2021 Scientific Reports  
To this direction, in this paper, an autoregressive COVID-19 prediction model with heterogeneous explanatory variables for Greece is proposed, taking past COVID-19 data, non-pharmaceutical interventions  ...  In specific, the prediction model identifies the 7-day lag that is needed in order for the measures' results to actually show, i.e., the optimal time-intervention framework for managing the disease's spread  ...  Data availability All data used in the analysis are open and publicly available in the cited sources. Received: 17 June 2020; Accepted: 30 April 2021  ... 
doi:10.1038/s41598-021-90293-5 pmid:34083549 fatcat:oc6blidppna4pht5xcka6hjoyu

Testing non-linearity and directedness of interactions between neural groups in the macaque inferotemporal cortex

Winrich A. Freiwald, Pedro Valdes, Jorge Bosch, Rolando Biscay, Juan Carlos Jimenez, Luis Manuel Rodriguez, Valia Rodriguez, Andreas K. Kreiter, Wolf Singer
1999 Journal of Neuroscience Methods  
In this paper a general framework that encompasses both linear and non-linear modelling of neurophysiological time series data by means of Local Linear Non-linear Autoregressive models (LLNAR) is described  ...  These tests assess the relative goodness of fit of linear versus non-linear models via the bootstrap technique.  ...  Acknowledgements The authors are grateful to Johanna Klon-Lipock and Petra Janson for excellent technical assistance.  ... 
doi:10.1016/s0165-0270(99)00129-6 pmid:10638819 fatcat:c4sqrh4lu5hb3m6jxkhb5w5v2e

Stochastic Modeling of Economic Injury Levels with Respect to Yearly Trends in Price Commodity

Petros Damos
2014 Journal of Insect Science  
Records, monitoring, documentation and check of success Table 1 . 1 Parameter estimates and model evaluation statistics for the stochastic autoregressive model (AR) in respect to model order in modelling  ...  More important, such kind of information is a prerequisite for further autoregressive modelling.  ... 
doi:10.1673/031.014.59 fatcat:mjgezivrcfbf7nqddtjw3fk5ve

Monitoring changes in the error distribution of autoregressive models based on Fourier methods

Zdeněk Hlávka, Marie Hušková, Claudia Kirch, Simos G. Meintanis
2011 Test (Madrid)  
We develop a procedure for monitoring changes in the error distribution of autoregressive time series while controlling the overall size of the sequential test.  ...  As it turns out, the procedure is not only able to detect distributional changes but also changes in the regression coefficient.  ...  Acknowlegements The work of Z. Hlávka was partially supported by MSM 0021620839, the work of M. Hušková was partially supported by the grants GACR 201/09/j006, 201/09/0755 and  ... 
doi:10.1007/s11749-011-0265-z fatcat:4b26ciwfo5fzrksmmb4pgrcs6e

Examining the Theoretical Framework of Behavioral Activation for Major Depressive Disorder: Smartphone-Based Ecological Momentary Assessment Study

Claire Rosalie van Genugten, Josien Schuurmans, Adriaan W Hoogendoorn, Ricardo Araya, Gerhard Andersson, Rosa Baños, Cristina Botella, Arlinda Cerga Pashoja, Roman Cieslak, David Daniel Ebert, Azucena García-Palacios, Jean-Baptiste Hazo (+11 others)
2021 JMIR Mental Health  
Longitudinal cross-lagged and cross-sectional associations of 240 patients were examined using random intercept cross-lagged panel models.  ...  Behavioral activation (BA), either as a stand-alone treatment or as part of cognitive behavioral therapy, has been shown to be effective for treating depression.  ...  For more in-depth information about the specifics of both country and setting recruitment procedures, please see elsewhere [35, 36] .  ... 
doi:10.2196/32007 pmid:34874888 pmcid:PMC8727050 fatcat:dq5ppn4aj5dfhceezs3vpqmc4u
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