Mining Sets of Time Series: Description of Time Points

2003 ◽  
pp. 41-49
Author(s):  
A. Dugarjapov ◽  
G. Lausen
Keyword(s):  
Time Series ◽  
Time Points

scholarly journals Extracting Features from Time Series

2018 ◽  
pp. 85-100 ◽  
Author(s):  
Christian Herff ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Clinical Data ◽  
Noise Filtering ◽  
Time Intervals ◽  
Time Points ◽  
Biomedical Systems ◽  
Patient Weight ◽  
Short Time

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

scholarly journals Phenomics data processing: A plot-level model for repeated measurements to extract the timing of key stages and quantities at defined time points

2021 ◽  
Author(s):  
Lukas Roth ◽  
María Xosé Rodríguez-Álvarez ◽  
Fred van Eeuwijk ◽  
Hans-Peter Piepho ◽  
Andreas Hund
Keyword(s):  
Time Series ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Measurement Techniques ◽  
Repeated Measurements ◽  
Canopy Height ◽  
Time Points ◽  
Percentile Method ◽  
Third Stage ◽  
Level Model

Decision-making in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as high-throughput field phenotyping (HTFP) hold promise to provide such insights. Processing HTFP data is an interdisciplinary task that requires broad knowledge on experimental design, measurement techniques, feature extraction, dynamic trait modeling, and prediction of genotypic values using statistical models. To get an overview of sources of variations in HTFP, we develop a general plot-level model for repeated measurements. Based on this model, we propose a seamless stage-wise process that allows to carry on estimated means and variances from stage to stage and approximates the gold standard of a single-stage analysis. The process builds on the extraction of three intermediate trait categories; (1) timing of key stages, (2) quantities at defined time points or periods, and (3) dose-response curves. In a first stage, these intermediate traits are extracted from low-level traits' time series (e.g., canopy height) using P-splines and the quarter of maximum elongation rate method (QMER), as well as final height percentiles. In a second and third stage, extracted traits are further processed using a stage-wise linear mixed model analysis. Using a wheat canopy growth simulation to generate canopy height time series, we demonstrate the suitability of the stage-wise process for traits of the first two above-mentioned categories. Results indicate that, for the first stage, the P-spline/QMER method was more robust than the percentile method. In the subsequent two-stage linear mixed model processing, weighting the second and third stage with error variance estimates from the previous stages improved the root mean squared error. We conclude that processing phenomics data in stages represents a feasible approach if using appropriate weighting through all stages. P-splines in combination with the QMER method are suitable tools to extract timing of key stages and quantities at defined time points from HTFP data.

scholarly journals Presentation of Coupling Analysis Techniques of Maximum and Minimum Values Between N Sets of Data Using Matrix [µ][MKN]

2021 ◽  
Vol 6 (4) ◽  
pp. 1127-1136
Author(s):  
K. N. Makris ◽  
I. Vonta
Keyword(s):  
Time Series ◽  
Indirect Method ◽  
Direct Method ◽  
Time Dependent ◽  
Data Sets ◽  
Data Display ◽  
Coupling Analysis ◽  
Time Points ◽  
Analysis Techniques ◽  
Coupling Techniques

This paper deals with the presentation and study of alternative coupling techniques for maximum and minimum values between data sets, namely the problem which is examined in this work is the possible appearance of maximum or minimum values between data sets in the same or neighboring time points. The data can be time-dependent (time series) or non-time-dependent. In this work, the analysis is focused on time series and novel indices are defined in order to measure whether the values of N sets of data display in terms of time, the maximum or minimum values at the same instances or at very close instances. For this purpose, two methods will be compared, one direct method and one indirect method. The indirect method is based on Matrices of dimensionless indicators which are denoted by [μ][MKN], and the direct method is based on a variance-type measure which is denoted by [V][MKN].

scholarly journals Comprehensive Time-Series Analysis of the Gene Expression Profile in a Susceptible Cultivar of Tree Tomato (Solanum betaceum) During the Infection of Phytophthora betacei

2021 ◽  
Vol 12 ◽  
Author(s):  
Daniel Bautista ◽  
Natalia Guayazan-Palacios ◽  
Maria Camila Buitrago ◽  
Martha Cardenas ◽  
David Botero ◽  
...  
Keyword(s):  
Time Series ◽  
Limiting Factors ◽  
Post Inoculation ◽  
Time Points ◽  
Pathogen Effectors ◽  
Pathogenesis Related ◽  
Transcriptional Changes ◽  
Series Study ◽  
Solanum Betaceum ◽  
Tree Tomato

Solanum betaceum is a tree from the Andean region bearing edible fruits, considered an exotic export. Although there has been renewed interest in its commercialization, sustainability, and disease management have been limiting factors. Phytophthora betacei is a recently described species that causes late blight in S. betaceum. There is no general study of the response of S. betaceum, particularly, in the changes in expression of pathogenesis-related genes. In this manuscript we present a comprehensive RNA-seq time-series study of the plant response to the infection of P. betacei. Following six time points of infection, the differentially expressed genes (DEGs) involved in the defense by the plant were contextualized in a sequential manner. We documented 5,628 DEGs across all time-points. From 6 to 24 h post-inoculation, we highlighted DEGs involved in the recognition of the pathogen by the likely activation of pattern-triggered immunity (PTI) genes. We also describe the possible effect of the pathogen effectors in the host during the effector-triggered response. Finally, we reveal genes related to the susceptible outcome of the interaction caused by the onset of necrotrophy and the sharp transcriptional changes as a response to the pathogen. This is the first report of the transcriptome of the tree tomato in response to the newly described pathogen P. betacei.

Dynamic Changes of Cognitive Habituation and Serotonin Metabolism During the Migraine Interval

Cephalalgia ◽  
1999 ◽  
Vol 19 (5) ◽  
pp. 485-491 ◽  
Author(s):  
S Evers ◽  
F Quibeldey ◽  
K-H Grotemeyer ◽  
B Suhr ◽  
I-W Husstedt
Keyword(s):  
Time Series ◽  
Migraine Attack ◽  
Cognitive Processing ◽  
Stable State ◽  
Event Related Potentials ◽  
Serotonin Metabolism ◽  
Dynamic Changes ◽  
Serotonin Content ◽  
Time Points ◽  
Related Potentials

Migraine patients show a specific cognitive processing with a loss of habituation in the interval and a normal habituation in the attack as measured by event-related potentials (ERPs). It is unknown whether the loss of habituation changes during the migraine interval or is a stable state. Serotonin (5HT) metabolism is involved in the pathophysiology of migraine and also in the generation of ERPs. We enrolled 14 patients with regular migraine attacks in order to measure visually evoked ERPs repetitively during the migraine interval and in the migraine attack. Cognitive habituation was evaluated by analysis of P3 latency. Platelet serotonin content and free serotonin plasma level were measured at the same time points. The loss of habituation increased continuously during the migraine interval and abruptly normalized in the migraine attack ( p < 0.05, time series analysis). The platelet 5HT content decreased significantly in the migraine attack ( p < 0.03) and was at its maximum in the middle of the interval. The P3 latency was significantly increased in the attack ( p < 0.01) and was significantly inversely correlated with the platelet 5HT content ( r= -0.44, p < 0.001). Free 5HT plasma levels did not show any significant change. Our findings suggest that loss of cognitive habituation continuously increases during the migraine interval until its normalization in the migraine attack. This phenomenon cannot be attributed to serotonergic transmission. In patients with regular changes of cognitive habituation before the migraine attack, it might be possible to predict the attack by analysing ERPs.

scholarly journals On the Construction of Bootstrap Confidence Intervals for Estimating the Correlation Between Two Time Series Not Sampled on Identical Time Points

2021 ◽  
Author(s):  
Mario Trottini ◽  
Isabel Vigo ◽  
Juan A. Vargas-Alemañy ◽  
David García-García ◽  
José Fernández
Keyword(s):  
Time Series ◽  
Correlation Function ◽  
Confidence Intervals ◽  
Cross Correlation ◽  
Asymptotic Properties ◽  
Cross Correlation Function ◽  
Bootstrap Confidence Intervals ◽  
Bootstrap Methods ◽  
Time Points ◽  
The Cross

AbstractTwo important issues characterize the design of bootstrap methods to construct confidence intervals for the correlation between two time series sampled (unevenly or evenly spaced) on different time points: (i) ordinary block bootstrap methods that produce bootstrap samples have been designed for time series that are coeval (i.e., sampled on identical time points) and must be adapted; (ii) the sample Pearson correlation coefficient cannot be readily applied, and the construction of the bootstrap confidence intervals must rely on alternative estimators that unfortunately do not have the same asymptotic properties. In this paper it is argued that existing proposals provide an unsatisfactory solution to issue (i) and ignore issue (ii). This results in procedures with poor coverage whose limitations and potential applications are not well understood. As a first step to address these issues, a modification of the bootstrap procedure underlying existing methods is proposed, and the asymptotic properties of the estimator of the correlation are investigated. It is established that the estimator converges to a weighted average of the cross-correlation function in a neighborhood of zero. This implies a change in perspective when interpreting the results of the confidence intervals based on this estimator. Specifically, it is argued that with the proposed modification of the bootstrap, the existing methods have the potential to provide a useful lower bound for the absolute correlation in the non-coeval case and, in some special cases, confidence intervals with approximately the correct coverage. The limitations and implications of the results presented are demonstrated with a simulation study. The extension of the proposed methodology to the problem of estimating the cross-correlation function is straightforward and is illustrated with a real data example. Related applications include the estimation of the autocorrelation function and the periodogram of a time series.

scholarly journals Echo state networks with multiple readout modules

2015 ◽  
Author(s):  
Andres Laan ◽  
Raul Vicente
Keyword(s):  
Time Series ◽  
General Purpose ◽  
Echo State Network ◽  
The Novel ◽  
Simple Linear Regression ◽  
Time Points ◽  
Echo State Networks ◽  
Performance Metric ◽  
Adaptive Step ◽  
Modified Algorithm

We propose a new readout architecture for echo state networks where multiple linear readout modules are activated at distinct time points to varying degrees by a separate controller module. The controller module, like the reservoir of the echo state network, can be initialized randomly. All linear readout modules are trained through simple linear regression, which is the only adaptive step in the modified algorithm. The resulting architecture provides modest improvements on a variety of time series processing tasks (between 5 to 50% in performance metric depending on the task studied). The novel architecture is guaranteed to perform at least as accurately as a conventional linear readout. It can be utilized as a general purpose readout method when augmentations to performance relative to the standard method is needed.PACS numbers: 05.45.Tp, 07.05.Mh

scholarly journals Persistent El Nino driven shifts in marine cyanobacteria populations

2021 ◽  
Author(s):  
Alyse Larkin ◽  
Allison Moreno ◽  
Adam Fagan ◽  
Adam Martiny
Keyword(s):  
Time Series ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Southern California Bight ◽  
Microbial Populations ◽  
Marine Cyanobacteria ◽  
Time Points ◽  
The North ◽  
Warm Anomaly ◽  
The North Pacific

&lt;p&gt;From 2014 through 2016, a significant El Ni&amp;#241;o event and the North Pacific warm anomaly (a.k.a., &amp;#8220;the blob&amp;#8221;) resulted in a marine heatwave across the Eastern North Pacific Ocean. To develop a deeper understanding of the impacts of El Ni&amp;#241;o on the Southern California Bight (SCB), we used coastal cyanobacteria populations in order to &amp;#8220;bi-directionally&amp;#8221; link shifts in microbial diversity and biogeochemical conditions. We sequenced the &lt;em&gt;rpo&lt;/em&gt;C1 gene from the ecologically important picocyanobacteria &lt;em&gt;Prochlorococcus&lt;/em&gt; and &lt;em&gt;Synechococcus&lt;/em&gt; at 434 time points from 2009&amp;#8211;2018 in the MICRO time series at Newport Beach, CA. Across the time series, we observed an increase in the abundance of &lt;em&gt;Prochlorococcus&lt;/em&gt; relative to &lt;em&gt;Synechococcus&lt;/em&gt; as well as elevated frequencies of clades commonly associated with low-nutrient and high-temperature conditions. The relationships between environmental and diversity trends appeared to operate on differing temporal scales. In addition, microdiverse populations from the &lt;em&gt;Prochlorococcous&lt;/em&gt; HLI clade as well as &lt;em&gt;Synechococcus&lt;/em&gt; Clade II that shifted in response to the 2015 El Ni&amp;#241;o did not return to their pre-heatwave composition by the end of this study. This research demonstrates that El Ni&amp;#241;o-driven warming in the SCB can result in persistent changes in key microbial populations.&lt;/p&gt;

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