scholarly journals Evaluating the Impact of Grazing Cessation and Reintroduction in Mixed Prairie Using Raster Time Series Analysis of Landsat Data

2021 ◽  
Vol 13 (17) ◽  
pp. 3397
Author(s):  
Dandan Xu ◽  
Jeff K. Harder ◽  
Weixin Xu ◽  
Xulin Guo
Keyword(s):  
Time Series ◽  
Native Vegetation ◽  
Vegetation Communities ◽  
Fresh Biomass ◽  
Effective Time ◽  
Green Cover ◽  
Time Periods ◽  
Biophysical Indicators ◽  
The Impact ◽  
Upland Grassland

Great efforts have been made to manage and restore native prairies to protect native species, enrich biodiversity, protect ecological resilience, and maintain ecosystem services. Much of this has been focused on preventing degradation from overgrazing and crop conversion. Understanding the consequences of management polices is important to identify best practices. Previous research has compared restoration outcomes from variable intensity grazing, prescribed fire, and grazing removal. However, few studies have explored the optimal durations of management practices and variation in restoration outcomes among vegetation communities. This study evaluates whether the impact of grazing cessation and reintroduction varies among native vegetation communities and measures the effective time periods of grazing cessation and reintroduction. Restoration outcomes were evaluated using four biophysical indicators (fresh biomass, soil organic matter, green cover, and litter cover) and two vegetation indices (normalized difference vegetation index (NDVI) and normalized difference water index (NDWI)) measured from Landsat images using seasonal Kalman filter and raster time series analysis. The results show that: (i) Grazing cessation increased soil organic matter and green cover while decreasing fresh biomass compared to moderate grazing management, while grazing reintroduction influences those indicators in an opposite direction; (ii) The effective time period for prairie conservation is about 11–14 years and varies among vegetation communities and biophysical indicators; (iii) The effective intensity of grazing cessation is highest in valley grassland, moderate in upland grassland, and mildest in sloped grassland; (iv) Grazing reintroduction returned the three native vegetation communities to the initial condition (i.e., the stage in 1985 before large grazers were removed), with less time than the time consumed for grazing cessation to restore the prairie ecosystem to the maximum changes; (v) Grazing reintroduction effectively influences upland and valley grasslands for 7 to 9 years, varying from different indicators, while it continuously affected sloped grassland with no clear time lag; (vi) The intensity of grazing reintroduction was strongest in sloped grassland, moderate in upland grassland, and mildest in valley grassland. The results of this study suggest expected time periods for prairie management methods to achieve results.

A Contextual Evaluation of Composite Forecasts of Annual Earnings

2016 ◽  
Vol 19 (03) ◽  
pp. 1650014 ◽  
Author(s):  
Pieter T. Elgers ◽  
May H. Lo ◽  
Wenjuan Xie ◽  
Le Emily Xu
Keyword(s):  
Time Series ◽  
Firm Size ◽  
Predictive Accuracy ◽  
Relative Accuracy ◽  
Regulation Fair Disclosure ◽  
Fair Disclosure ◽  
Time Periods ◽  
Time Specific ◽  
Annual Earnings ◽  
The Impact

This study addresses the impact of firm- and time-specific attributes on the accuracy of composite forecasts of annual earnings, constructed from time-series, price-based, and analysts' forecasts. The attributes examined include firm size, analysts' coverage, and time periods pre-dating and following the implementation of regulation fair disclosure. Our results indicate that the relative accuracy of the composite forecasts is time-specific. In the pre-regulation fair disclosure period, composite forecasts significantly outperform each of the three individual forecast sources. Moreover, the extent of improvement in accuracy of composite forecasts is significantly higher for the smaller and lightly-covered firms. Collectively, these results suggest that the predictive accuracy of composite forecasts is contextual.

scholarly journals Measuring the Impact of Financial News and Social Media on Stock Market Modeling Using Time Series Mining Techniques

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 181 ◽  
Author(s):  
Foteini Kollintza-Kyriakoulia ◽  
Manolis Maragoudakis ◽  
Anastasia Krithara
Keyword(s):  
Social Media ◽  
Time Series ◽  
Time Series Data ◽  
Technical Analysis ◽  
Series Data ◽  
Textual Information ◽  
Public Opinions ◽  
Time Periods ◽  
The Impact ◽  
Matching Techniques

In this work, we study the task of predicting the closing price of the following day of a stock, based on technical analysis, news articles and public opinions. The intuition of this study lies in the fact that technical analysis contains information about the event, but not the cause of the change, while data like news articles and public opinions may be interpreted as a cause. The paper uses time series analysis techniques such as Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) to study the existence of a relation between price data and textual information, either from news or social media. Pattern matching techniques from time series data are also incorporated, in order to experimentally validate potential correlations of price and textual information within given time periods. The ultimate goal is to create a forecasting model that exploits the previously discovered patterns in order to augment the forecasting accuracy. Results obtained from the experimental phase are promising. The performance of the classifier shows clear signs of improvement and robustness within the time periods where patterns between stock price and the textual information have been identified, compared to the periods where patterns did not exist.

Experimental Investigation of the Impact of Dual Time Periods on Crash Hazardous Location Identification

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Gilbert Leano ◽  
Wen Cheng ◽  
Xudong Jia ◽  
Lingqi Kong ◽  
Robert Brennan
Keyword(s):  
Experimental Investigation ◽  
Time Periods ◽  
Location Identification ◽  
The Impact

River-Basin Planning for Control of Nitrate Pollution

1982 ◽  
Vol 14 (4-5) ◽  
pp. 245-252 ◽  
Author(s):  
C S Sinnott ◽  
D G Jamieson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
River Basin ◽  
Water Resource ◽  
Potable Water ◽  
Potential Problem ◽  
Nitrate Pollution ◽  
Optimal Mix ◽  
Regional Basis ◽  
The Impact

The combination of increasing nitrate concentrations in the River Thames and the recent EEC Directive on the acceptable level in potable water is posing a potential problem. In assessing the impact of nitrates on water-resource systems, extensive use has been made of time-series analysis and simulation. These techniques are being used to define the optimal mix of alternatives for overcoming the problem on a regional basis.

Análise da relação entre O3, NO e NO2 usando técnicas de regressão linear múltipla.

GEOgraphia ◽  
2018 ◽  
Vol 20 (43) ◽  
pp. 124
Author(s):  
Amaury De Souza ◽  
Priscilla V Ikefuti ◽  
Ana Paula Garcia ◽  
Debora A.S Santos ◽  
Soetania Oliveira
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Nitrogen Dioxide ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Environmental Pollutant ◽  
Environmental Research ◽  
Linear Regression Models ◽  
Mato Grosso ◽  
The Impact

Análise e previsão de parâmetros de qualidade do ar são tópicos importantes da pesquisa atmosférica e ambiental atual, devido ao impacto causado pela poluição do ar na saúde humana. Este estudo examina a transformação do dióxido de nitrogênio (NO2) em ozônio (O3) no ambiente urbano, usando o diagrama de séries temporais. Foram utilizados dados de concentração de poluentes ambientais e variáveis meteorológicas para prever a concentração de O3 na atmosfera. Foi testado o emprego de modelos de regressão linear múltipla como ferramenta para a predição da concentração de O3. Os resultados indicam que o valor da temperatura e a presença de NO2 influenciam na concentração de O3 em Campo Grande, capital do Estado do Mato Grosso do Sul. Palavras-chave: Ozônio. Dióxido de nitrogênio. Séries cronológicas. Regressões. ANALYSIS OF THE RELATIONSHIP BETWEEN O3, NO AND NO2 USING MULTIPLE LINEAR REGRESSION TECHNIQUES.Abstract: Analysis and prediction of air quality parameters are important topics of current atmospheric and environmental research due to the impact caused by air pollution on human health. This study examines the transformation of nitrogen dioxide (NO2) into ozone (O3) in the urban environment, using the time series diagram. Environmental pollutant concentration and meteorological variables were used to predict the O3 concentration in the atmosphere. The use of multiple linear regression models was tested as a tool to predict O3 concentration. The results indicate that the temperature value and the presence of NO2 influence the O3 concentration in Campo Grande, capital of the State of Mato Grosso do Sul.Keywords: Ozone. Nitrogen dioxide. Time series. Regressions. ANÁLISIS DE LA RELACIÓN ENTRE O3, NO Y NO2 UTILIZANDO MÚLTIPLES TÉCNICAS DE REGRESIÓN LINEAL.Resumen: Análisis y previsión de los parámetros de calidad del aire son temas importantes de la actual investigación de la atmósfera y el medio ambiente, debido al impacto de la contaminación atmosférica sobre la salud humana. Este estudio examina la transformación del dióxido de nitrógeno (NO2) en ozono (O3) en el entorno urbano, utilizando el diagrama de series de tiempo. Las concentraciones de los contaminantes ambientales de datos y variables climáticas fueron utilizadas para predecir la concentración de O3 en la atmósfera. El uso de múltiples modelos de regresión lineal como herramienta para predecir la concentración de O3 se puso a prueba. Los resultados indican que el valor de la temperatura y la presencia de NO2 influyen en la concentración de O3 en Campo Grande, capital del Estado de Mato Grosso do Sul.Palabras clave: Ozono. Dióxido de nitrógeno. Series de tiempo. Regresiones.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

scholarly journals Correction to: Measuring the impact of the Afordable Care Act Medicaid expansion on access to primary care using an interrupted time series approach

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Elizabeth A. Brown ◽  
Brandi M. White ◽  
Walter J. Jones ◽  
Mulugeta Gebregziabher ◽  
Kit N. Simpson
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Interrupted Time Series ◽  
Medicaid Expansion ◽  
Time Series Approach ◽  
Access To Primary Care ◽  
The Impact

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals Does admission prevalence change after reconfiguration of inpatient services? An interrupted time series analysis of the impact of reconfiguration in five centres

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Joanne Martin ◽  
Edwin Amalraj Raja ◽  
Steve Turner
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Emergency Admission ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Emergency Admissions ◽  
Series Analysis ◽  
Inpatient Services ◽  
The Impact ◽  
Admission Prevalence

Abstract Background Service reconfiguration of inpatient services in a hospital includes complete and partial closure of all emergency inpatient facilities. The “natural experiment” of service reconfiguration may give insight into drivers for emergency admissions to hospital. This study addressed the question does the prevalence of emergency admission to hospital for children change after reconfiguration of inpatient services? Methods There were five service reconfigurations in Scottish hospitals between 2004 and 2018 where emergency admissions to one “reconfigured” hospital were halted (permanently or temporarily) and directed to a second “adjacent” hospital. The number of emergency admissions (standardised to /1000 children in the regional population) per month to the “reconfigured” and “adjacent” hospitals was obtained for five years prior to reconfiguration and up to five years afterwards. An interrupted time series analysis considered the association between reconfiguration and admissions across pairs comprised of “reconfigured” and “adjacent” hospitals, with adjustment for seasonality and an overall rising trend in admissions. Results Of the five episodes of reconfiguration, two were immediate closure, two involved closure only to overnight admissions and one with overnight closure for a period and then closure. In “reconfigured” hospitals there was an average fall of 117 admissions/month [95% CI 78, 156] in the year after reconfiguration compared to the year before, and in “adjacent” hospitals admissions rose by 82/month [32, 131]. Across paired reconfigured and adjacent hospitals, in the months post reconfiguration, the overall number of admissions to one hospital pair slowed, in another pair admissions accelerated, and admission prevalence was unchanged in three pairs. After reconfiguration in one hospital, there was a rise in admissions to a third hospital which was closer than the named “adjacent” hospital. Conclusions There are diverse outcomes for the number of emergency admissions post reconfiguration of inpatient facilities. Factors including resources placed in the community after local reconfiguration, distance to the “adjacent” hospital and local deprivation may be important drivers for admission pathways after reconfiguration. Policy makers considering reconfiguration might consider a number of factors which may be important determinants of admissions post reconfiguration.

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