scholarly journals Seasonal variation of diel vertical migration of zooplankton from ADCP backscatter time series data in the Lazarev Sea, Antarctica

2010 ◽  
Vol 57 (1) ◽  
pp. 78-94 ◽  
Author(s):  
Boris Cisewski ◽  
Volker H. Strass ◽  
Monika Rhein ◽  
Sören Krägefsky
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Vertical Migration ◽  
Diel Vertical Migration ◽  
Time Series Data ◽  
Series Data

Seasonal variation in liver function tests: a time-series analysis of outpatient data

2009 ◽  
Vol 46 (5) ◽  
pp. 377-384 ◽  
Author(s):  
Kazunori Miyake ◽  
Noriko Miyake ◽  
Shigemi Kondo ◽  
Yoko Tabe ◽  
Akimichi Ohsaka ◽  
...  
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Liver Function ◽  
Seasonal Variations ◽  
Time Series Data ◽  
Liver Function Tests ◽  
Series Data ◽  
Test Results ◽  
Men And Women ◽  
Function Tests

Background Long-term physiological variations, such as seasonal variations, affect the screening efficiency at medical checkups. This study examined the seasonal variation in liver function tests using recently described data-mining methods. Methods The ‘latent reference values’ of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma-glutamyltransferase ( γGT), cholinesterase (ChE) and total bilirubin (T-Bil) were extracted from a seven-year database of outpatients (aged 20–79 yr; comprising approximately 1,270,000 test results). After calculating the monthly means for each variable, the time-series data were separated into trend and seasonal components using a local regression model (Loess method). Then, a cosine function model (cosinor method) was applied to the seasonal component to determine the periodicity and fluctuation range. A two-year outpatient database (215,000 results) from another hospital was also analysed to confirm the reproducibility of these methods. Results The serum levels of test results tended to increase in the winter. The increase in AST and ALT was about 6% in men and women, and was greater than that in ChE, ALP (in men and women) and γGT (in men). In contrast, T-Bil increased by 3.6% (men) and 5.0% (women) in the summer. The total protein and albumin concentrations did not change significantly. AST and ALT showed similar seasonal variation in both institutions in the comparative analysis. Conclusions The liver function tests were observed to show seasonal variations. These seasonal variations should therefore be taken into consideration when establishing either reference intervals or cut-off values, which are especially important regarding aminotransferases.

Anomaly Detection by STL Decomposition and Extended Isolation Forest on Environmental Univariate Time Series

2020 ◽  
Author(s):  
İsmail Sezen ◽  
Alper Unal ◽  
Ali Deniz
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Complex Structure ◽  
Series Data ◽  
High Concentration ◽  
Univariate Time Series ◽  
Concentration Levels ◽  
Isolation Forest

<p>Atmospheric pollution is one of the primary problems and high concentration levels are critical for human health and environment. This requires to study causes of unusual high concentration levels which do not conform to the expected behavior of the pollutant but it is not always easy to decide which levels are unusual, especially, when data is big and has complex structure. A visual inspection is subjective in most cases and a proper anomaly detection method should be used. Anomaly detection has been widely used in diverse research areas, but most of them have been developed for certain application domains. It also might not be always a good idea to identify anomalies by using data from near measurement sites because of spatio-temporal complexity of the pollutant. That’s why, it’s required to use a method which estimates anomalies from univariate time series data.</p><p>This work suggests a framework based on STL decomposition and extended isolation forest (EIF), which is a machine learning algorithm, to identify anomalies for univariate time series which has trend, multi-seasonality and seasonal variation. Main advantage of EIF method is that it defines anomalies by a score value.</p><p>In this study, a multi-seasonal STL decomposition has been applied on a univariate PM10 time series to remove trend and seasonal parts but STL is not resourceful to remove seasonal variation from the data. The remainder part still has 24 hours and yearly variation. To remove the variation, hourly and annual inter-quartile ranges (IQR) are calculated and data is standardized by dividing each value to corresponding IQR value. This process ensures removing seasonality in variation and the resulting data is processed by EIF to decide which values are anomaly by an objective criterion.</p>

Detection and Analysis of Small-Amplitude Seasonal Variation in a Short Time Series

2018 ◽  
Vol 64 (1) ◽  
pp. 73-81
Author(s):  
Osvaldo Marrero
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Data Analysis ◽  
Medical Research ◽  
Small Amplitude ◽  
Time Series Data ◽  
Series Data ◽  
Short Time Series ◽  
Short Time ◽  
Jel Classifications

We discuss a procedure for seasonality analyses of short time-series data with small amplitude. Such analyses are often performed in medical research. In economics, however, time series are typically long and of appreciable amplitude; therefore, economists are used to analyzing such data. Our procedure provides one more tool for the economists’ data-analysis toolbox. We illustrate the procedure’s application with three examples of real economics data. The examples demonstrate that the procedure can be profitably applied to short economics time series. JEL Classifications: C12, C22, C23, C49, F31

scholarly journals Seasonality of Tuberculosis in Delhi, India: A Time Series Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Varun Kumar ◽  
Abhay Singh ◽  
Mrinmoy Adhikary ◽  
Shailaja Daral ◽  
Anita Khokhar ◽  
...  
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Autocorrelation Function ◽  
Time Series Data ◽  
Seasonal Pattern ◽  
Cost Effective ◽  
Series Data ◽  
Suitable Model ◽  
Short Course ◽  
Directly Observed Treatment

Background. It is highly cost effective to detect a seasonal trend in tuberculosis in order to optimize disease control and intervention. Although seasonal variation of tuberculosis has been reported from different parts of the world, no definite and consistent pattern has been observed. Therefore, the study was designed to find the seasonal variation of tuberculosis in Delhi, India.Methods. Retrospective record based study was undertaken in a Directly Observed Treatment Short course (DOTS) centre located in the south district of Delhi. Six-year data from January 2007 to December 2012 was analyzed. Expert modeler of SPSS ver. 21 software was used to fit the best suitable model for the time series data.Results. Autocorrelation function (ACF) and partial autocorrelation function (PACF) at lag 12 show significant peak suggesting seasonal component of the TB series. Seasonal adjusted factor (SAF) showed peak seasonal variation from March to May. Univariate model by expert modeler in the SPSS showed that Winter’s multiplicative model could best predict the time series data with 69.8% variability. The forecast shows declining trend with seasonality.Conclusion. A seasonal pattern and declining trend with variable amplitudes of fluctuation were observed in the incidence of tuberculosis.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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