scholarly journals Central Neurochemical Ultradian Variability in Depression

2006 ◽  
Vol 22 (1-2) ◽  
pp. 65-72 ◽  
Author(s):  
Ronald M. Salomon ◽  
Benjamin W. Johnson ◽  
Dennis E. Schmidt
Keyword(s):  
Repeated Measures ◽  
Time Series Data ◽  
Antidepressant Treatment ◽  
Series Data ◽  
Time Variability ◽  
Data Series ◽  
Monoamine Metabolites ◽  
Intrinsic Variability ◽  
Neuroendocrine Function ◽  
Depressed Patients

Depression is characterized by blunted behavior and neuroendocrine function that generally improve with antidepressant treatment. This study examined intrinsic variability in brain neurotransmitter function, since it may be a source of blunted behavior and neuroendocrine function in depression and a marker for the illness, and has not previously been analyzed using wavelet decomposition. To measure variability in monoamine metabolites, lumbar cerebrospinal fluid (CSF) was collected in serial samples in depressed patients before and after treatment. We hypothesized that changes in variability would be observed after treatment. Mechanisms that control such variability may be critical to the pathophysiology of depression.Method:Time series data was obtained from serial ten-min sampling over a 24-hr period (N= 144) from thirteen depressed patients, with a repeat collection after 5 weeks of antidepressant (sertraline or bupropion) treatment. Concentrations of tryptophan (TRP), the monoamine metabolites 5-HIAA (metabolite of serotonin) and HVA (metabolite of dopamine), and the HVA:5HIAA ratio were transformed to examine power in slowly (160 min/cycle) to rapidly (20 min/cycle) occurring events. Power, the sum of the squares of the coefficients in each d (detail) wavelet, reflects variability within a limited frequency bandwidth for that wavelet. Pre-treatment to post-treatment comparisons were conducted with repeated measures ANOVA.Results:Antidepressant treatment was associated with increased power in the d2 wavelet from the HVA (p= 0.03) and the HVA:5-HIAA ratio (p= 0.03) series. The d1 and d3 wavelets showed increased power following antidepressant treatment for the ratio series (d1,p= 0.01; d3,p= 0.05). Significant changes in power were not observed for the 5-HIAA data series. Power differences among analytes suggest that the findings are specific to each system.Conclusion:The wavelet transform analysis shows changes in neurochemical signal variability following antidepressant treatment. Patterns or degrees of variability may be as important as, or possibly more important than, the mean levels of monoamine transmitters. Studies of variability observed in healthy individuals and a larger depressed sample will be needed to verify a relationship with mood and treatment response. Neurochemical measures of time-variability may be a pivotal marker in depression.

scholarly journals Statistical Parametric Mapping Reveals Subtle Gender Differences in Angular Movements in Table Tennis Topspin Backhand

2020 ◽  
Vol 17 (19) ◽  
pp. 6996
Author(s):  
Ziemowit Bańkosz ◽  
Sławomir Winiarski
Keyword(s):  
Gender Differences ◽  
Time Series Data ◽  
Statistical Parametric Mapping ◽  
Study Groups ◽  
Series Data ◽  
Data Series ◽  
Table Tennis ◽  
Parametric Mapping ◽  
Analysis System ◽  
Dorsal Flexion

Background: Statistical parametric mapping (SPM) is an innovative method based on the analysis of time series (data series) and is equivalent to statistical methods for numerical (discrete) data series. This study aimed to analyze the patterns of movement in the topspin backhand stroke in table tennis and to use SPM to compare these patterns between advanced female and male players. Methods: The research involved seven advanced male and six advanced female players. The kinematic parameters were measured using an inertial motion analysis system. The SPM was computed using the SPM1D Python package. Results: Our study made it possible to reproduce the pattern of movement in the joints during topspin backhand strokes in the studied athletes. During multiple comparisons, the analysis of variance (ANOVA) SPM test revealed many areas in the studied parameter series with statistically significant differences (p ≤ 0.01). Conclusions: The study presents the movement patterns in the topspin backhand shot and describes the proximal-to-distal sequencing principle during this shot. The SPM study revealed differences between men and women in the contribution of thoracic rotation, external shoulder rotation, dorsal flexion, and supination in the wrist during the hitting phase. These differences may result from the anatomical gender differences or variations in other functionalities of individual body segments between the study groups. Another possible source for these discrepancies may reside in tactical requirements, especially the need for a more vigorous attack in men. The gender differences presented in this study can help in the individualization of the training process in table tennis.

scholarly journals Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

2020 ◽  
Vol 12 (17) ◽  
pp. 2735 ◽  
Author(s):  
Carlos M. Souza ◽  
Julia Z. Shimbo ◽  
Marcos R. Rosa ◽  
Leandro L. Parente ◽  
Ane A. Alencar ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Google Earth ◽  
Series Data ◽  
Data Series ◽  
Forest Conversion ◽  
Pixel Resolution ◽  
Historical Land Use

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

scholarly journals Penerapan Ukuran Ketepatan Nilai Ramalan Data Deret Waktu dalam Seleksi Model Peramalan Volume Penjualan PT Satriamandiri Citramulia

2011 ◽  
Vol 2 (2) ◽  
pp. 636 ◽  
Author(s):  
Iwa Sungkawa ◽  
Ries Tri Megasari
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Data Series ◽  
Past Data ◽  
The Mean

Forecasting is performed due to the complexity and uncertainty faced by a decision maker. This article discusses the selection of an appropriate forecasting model with time series data available. An appropriate forecasting model is required to estimate systematically about what is most likely to occur in the future based on past data series, so that errors (the differences between what actually happens and the results of the estimation) can be minimized. A gauge is required to detect the required the value of forecast accuracy. In this paper ways of forecasting accuracy of detection are discussed using the mean square error (MSE) and the mean absolute percentage error (MAPE). The forecasting method uses Moving Average, Exponential Smoothing, and Winters method. With the three methods forecast value is determined and the smallest value of MSE and Mape is selected. The results of data analysis showed that the Exponential Smoothing is considered an appropriate method to forecast the sales volume of PT Satriamandiri Citramulia because it produces the smallest value of MSE and Mape. 

scholarly journals The Pressing Need to Test for Autocorrelation: Comparison of Repeated Measures ANOVA and Interrupted Time Series Autoregressive Models

2019 ◽  
Vol 9 (2) ◽  
pp. 22-31 ◽  
Author(s):  
Jay Schyler Raadt
Keyword(s):  
Time Series ◽  
Repeated Measures ◽  
Time Series Data ◽  
Longitudinal Research ◽  
Moving Average ◽  
Interrupted Time Series ◽  
Series Data ◽  
Repeated Measures Analysis ◽  
Scientific Results ◽  
Simulated Time

Neglecting to measure autocorrelation in longitudinal research methods such as Repeated Measures (RM) ANOVA produces invalid results. Using simulated time series data varying on autocorrelation, this paper compares the performance of repeated measures analysis of variance (RM ANOVA) to interrupted time series autoregressive integrated moving average (ITS ARIMA) models, which explicitly model autocorrelation. Results show that the number of RM ANOVA signaling an intervention effect increase as autocorrelation increases whereas this relationship is opposite using ITS ARIMA. This calls the use of RM ANOVA for longitudinal educational research into question as well as past scientific results that used this method, exhorting educational researchers to investigate the use of ITS ARIMA.

scholarly journals An ECM Approach for Long Run Relationship Between Real Exchange Rate and Output Growth: Evidence from Bangladesh

2015 ◽  
Vol 63 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Khnd Md Mostafa Kamal
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Real Exchange Rate ◽  
Time Series Data ◽  
Output Growth ◽  
Series Data ◽  
Data Series ◽  
Currency Exchange ◽  
Long Run ◽  
Short Run

Currency exchange rate is an important aspect in modern economy which indicates the strength of domestic currency with respect to international currency. This study uses 42 years’ (1972 to 2013) time series data for Bangladesh in order to empirically determine whether the real exchange rate has significant impact on output growth for Bangladesh by using error correction model (ECM).The time series econometrics properties of the data series have been thoroughly investigated to apply ECM approach. The empirical evidence suggests mixed results; in the short run low exchange rate has positive significant effect while in the long run output growth is positively affected high exchange rate pass through.Dhaka Univ. J. Sci. 63(2):105-110, 2015 (July)

scholarly journals Time Series Outlier Detection Based on Sliding Window Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yufeng Yu ◽  
Yuelong Zhu ◽  
Shijin Li ◽  
Dingsheng Wan
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Time Series Data ◽  
Series Data ◽  
Forecasting Model ◽  
Data Series ◽  
Hydrologic Time Series ◽  
Prediction Confidence ◽  
Evaluation Of Data ◽  
Operation And Management

In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use ofPCIas threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

scholarly journals Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Miguel García ◽  
José Alloza ◽  
Ángeles Mayor ◽  
Susana Bautista ◽  
Francisco Rodríguez
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Mediterranean Region ◽  
Time Series Data ◽  
Series Data ◽  
Data Series ◽  
Regional Government ◽  
Fire Event ◽  
Fire Scar ◽  
Burnt Areas

AbstractModerate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

EFFECT OF OUTLIERS ON THE FRACTAL DIMENSION ESTIMATION

Fractals ◽  
2011 ◽  
Vol 19 (02) ◽  
pp. 233-241
Author(s):  
SHAPOUR MOHAMMADI
Keyword(s):  
Fractal Dimension ◽  
Stock Prices ◽  
Time Series Data ◽  
Estimation Method ◽  
Estimation Methods ◽  
Series Data ◽  
Data Series ◽  
Box Dimension ◽  
Dimension Estimation ◽  
Stochastic Data

The effect of outliers on estimation of the fractal dimension of experimental chaotic and stock market stochastic data series is investigated. The results indicate that influential observations of a magnitude of mean ±5 standard deviations can lead to a distortion of fractal dimension estimations by as much as 40% for short (e.g. 500 observations) time series data. Moreover, the box dimension estimation method is more sensitive to outliers than information and correlation dimension estimation methods and the effect of outliers decreases as the number of observations increases. Application of outlier adjustment to the stock prices of 60 companies of the Dow Jones Industrial Index reveals that the effect of outliers is critical in estimating the fractal dimension. The fractal dimension has applications in risk analysis for financial markets that can be affected by outliers.

scholarly journals Modelling aboveground net primary production (ANPP) of an Atlantic mountain grassland based on time series approach

2019 ◽  
Vol 45 (2) ◽  
pp. 551
Author(s):  
A. Salaberria ◽  
G. García-Baquero ◽  
I. Odriozola ◽  
A. Aldezabal
Keyword(s):  
Time Series ◽  
Primary Production ◽  
Time Series Data ◽  
Net Primary Production ◽  
Series Data ◽  
Data Series ◽  
Treatment Level ◽  
Aboveground Net Primary Production ◽  
Smoothing Methods ◽  
Mountain Grassland

Because primary productivity is related both with the energy that sustains food webs and with species diversity, it is usually considered a key ecosystem property and a reliable indicator of available forage. In this work the aboveground net primary production (ANPP) of an Atlantic mountain grassland system was modelled in order to attempt producing short-term forecasts. Since grazing influences productivity, two treatment levels (grazing and exclusion) were experimentally applied in each of three field sites. Monthly ANPP data were then collected over three consecutive vegetative periods (2006-2008), thereby obtaining six time series (one per plot). Since no significant differences among sites (within treatments) were found, these six series were later reduced through averaging to only two series (one per treatment level). Two kinds of statistical models were then used to attempt monthly ANPP forecasting: exponential smoothing methods and ARIMA models. Both methodologies turned out to produce inadequate forecasts due to the presence of marked local features (innovative outliers) in our relatively short time-series data. Nonetheless, useful information for a more innovative shepherding management was revealed (e.g. the presence of within-year variation in ANPP, and differences between the grazing and exclusion treatments). Longer data series, which would require a more demanding effort in sampling investment, are likely necessary in order to obtain adequate forecasts using these time series methodologies.

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