Practical Analysis of Time Series

2020 ◽  
pp. 45-60
Author(s):  
Victor Privalsky
Keyword(s):  
Time Series ◽  
Practical Analysis ◽  
Analysis Of Time Series

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

scholarly journals Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

1984 ◽  
Vol 30 (104) ◽  
pp. 66-76 ◽  
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons ◽  
N. Ahmad ◽  
Gordon Smith ◽  
M. Pourchet
Keyword(s):  
Time Series ◽  
Chemical Species ◽  
Data Sets ◽  
Reactive Iron ◽  
Physical Time ◽  
Ladakh Himalaya ◽  
Data Density ◽  
Mass Circulation ◽  
The Himalaya ◽  
Analysis Of Time Series

AbstractSpectral analysis of time series of a c. 17 ± 0.3 year core, calibrated for total ß activity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium, δD, δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70 μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

Analysis of Time Series Models for Brazilian Electricity Demand Forecasting

2021 ◽  
Author(s):  
Carlos Eduardo Velasquez Cabrera ◽  
Matheus Zocatelli ◽  
Fidellis B.G.L. e Estanislau ◽  
Victor Faria
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Time Series Models ◽  
Electricity Demand Forecasting ◽  
Analysis Of Time Series

Preliminary results of the first assessment of 20 years of dust activity in the Patagonia desert (South America) with aerosol products from the MODIS sensors

2021 ◽  
Author(s):  
Santiago Gassó ◽  
Pawan Gupta ◽  
Paul Ginoux ◽  
Robert Levy
Keyword(s):  
Time Series ◽  
South America ◽  
Volcanic Eruptions ◽  
Transport Processes ◽  
Dust Storms ◽  
Biological Processes ◽  
Aerosol Transport ◽  
Deep Blue ◽  
Fire Activity ◽  
Analysis Of Time Series

<p>Aerosol transport processes in the Southern Hemisphere (SH) have been the center of renewed attention in the last two decades because of a number of major geophysical events such as volcanic eruptions (Chile and Argentina), biomass burning (Australia and Chile) and dust storms (Australia and Argentina).<br><br>While volcanic and fire activity in the SH have been the focus of several studies, there is a dearth of satellite assessments of dust activity. The lack of such analysis impairs the understanding of biological processes in the Southern Ocean and of the provenance of dust found in snow at the surface of East Antarctica.<br><br>This presentation will show an analysis of time series of Aerosol Optical Depths over the Patagonia desert in South America. Data from two aerosol algorithms (Dark Target and Deep Blue) will be jointly analyzed to establish a timeline of dust activity in the region. Also, dust proxies from both algorithms will be compared with ground-based observations of visibility at different airports in the area. Once an understanding of frequency and time evolution of the dust activity is achieved, first estimations of ocean-going dust fluxes will be derived.</p>

Analysis of time series using Big Data algorithms

2021 ◽  
pp. 118-124
Author(s):  
А.И. Сотников
Keyword(s):  
Time Series ◽  
Big Data ◽  
Daily Basis ◽  
Processing Methods ◽  
Traditional Technologies ◽  
New Processing ◽  
Analysis Of Time Series

То, с какой скоростью человечество накапливает информацию ежедневно, и непредсказуемость завтрашнего дня показывают, что для прогнозирования временных рядов больших данных уже не хватает традиционных технологий и необходимы новые методы обработки. В связи с этим встает вопрос, какие методы возможно использовать в настоящее время для получения достоверного прогнозирования временных рядов больших данных? The speed at which humanity is accumulating information on a daily basis and the unpredictability of tomorrow show that traditional technologies are no longer enough for forecasting big data time series and new processing methods are needed. In this regard, the question arises, what methods can be used at present to obtain reliable forecasting of time series of big data?

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