Temporal trends in δ18O composition of precipitation in Germany: insights from time series modelling and trend analysis

This study investigated the temporal patterns of annual and seasonal river runoff data at 13 hydrological stations in the Lake Issyk-Kul basin, Central Asia. The temporal trends were analyzed using the innovative trend analysis (ITA) method with significance testing. The ITA method results were compared with the Mann-Kendall (MK) trend test at a 95% confidence level. The comparison results revealed that the ITA method could effectively identify the trends detected by the MK trend test. Specifically, the MK test found that the time series percentage decreased from 46.15% in the north to 25.64% in the south, while the ITA method revealed a similar rate of decrease, from 39.2% to 29.4%. According to the temporal distribution of the MK test, significantly increasing (decreasing) trends were observed in 5 (0), 6 (2), 4 (3), 8 (0), and 8 (1) time series in annual, spring, summer, autumn, and winter river runoff data. At the same time, the ITA method detected significant trends in 7 (1), 9 (3), 6(3), 9 (3), and 8 (2) time series in the study area. As for the ITA method, the “peak” values of 24 time series (26.97%) exhibited increasing patterns, 25 time series (28.09%) displayed increasing patterns for “low” values, and 40 time series (44.94%) showed increasing patterns for “medium” values. According to the “low”, “medium”, and “peak” values, five time series (33.33%), seven time series (46.67%), and three time series (20%) manifested decreasing trends, respectively. These results detailed the patterns of annual and seasonal river runoff data series by evaluating “low”, “medium”, and “peak” values.

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Finnish National Phenological Network 1997–2017: from observations to trend detection

International Journal of Biometeorology ◽

10.1007/s00484-020-01961-6 ◽

2020 ◽

Vol 64 (10) ◽

pp. 1783-1793

Author(s):

Samuli Helama ◽

Anne Tolvanen ◽

Jouni Karhu ◽

Jarmo Poikolainen ◽

Eero Kubin

Keyword(s):

Time Series ◽

Trend Analysis ◽

Temporal Trends ◽

Early Summer ◽

Trend Detection ◽

Phenological Stages ◽

Study Region ◽

Single Tree ◽

Multiple Observations ◽

Multiple Trees

Abstract Plant phenological dataset collected at 42 sites across the mainland of Finland and covering the years 1997–2017 is presented and analysed for temporal trends. The dataset of n = 16,257 observations represents eleven plant species and fifteen phenological stages and results in forty different variables, i.e. phenophases. Trend analysis was carried out for n = 808 phenological time-series that contained at least 10 observations over the 21-year study period. A clear signal of advancing spring and early-summer phenology was detected, 3.4 days decade−1, demonstrated by a high proportion of negative trends for phenophases occurring in April through June. Latitudinal correlation indicated stronger signal of spring and early-summer phenology towards the northern part of the study region. The autumn signal was less consistent and showed larger within-site variations than those observed in other seasons. More than 60% of the dates based on single tree/monitoring square were exactly the same as the averages from multiple trees/monitoring squares within the site. In particular, the reliability of data on autumn phenology was increased by multiple observations per site. The network is no longer active.

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SARS spatial data analysis and time series modelling

Advanced Control, Automation and Robotics ◽

10.2495/acar140771 ◽

2015 ◽

Author(s):

M. Zhang ◽

J.H. Wen ◽

Y.L. Fan ◽

S.J. Jin

Keyword(s):

Time Series ◽

Data Analysis ◽

Spatial Data ◽

Spatial Data Analysis ◽

Time Series Modelling

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Time series trend analysis of rainfall in last five decades and its quantification in Aseer Region of Saudi Arabia

Arabian Journal of Geosciences ◽

10.1007/s12517-021-06935-5 ◽

2021 ◽

Vol 14 (6) ◽

Author(s):

Majed AlSubih ◽

Madhuri Kumari ◽

Javed Mallick ◽

Raghu Ramakrishnan ◽

Saiful Islam ◽

...

Keyword(s):

Time Series ◽

Saudi Arabia ◽

Trend Analysis

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Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

Engineering Proceedings ◽

10.3390/engproc2021005017 ◽

2021 ◽

Vol 5 (1) ◽

pp. 17

Author(s):

Miguel Ángel Ruiz Reina

Keyword(s):

Time Series ◽

Time Series Data ◽

Prognostic Models ◽

Series Data ◽

Economic Agents ◽

Time Series Modelling ◽

Simulation And Training ◽

Uncertainty Method ◽

And Training ◽

Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

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1120. Impact of Training Residents to Improve HIV Screening in a Teaching Hospital in Mexico City

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1306 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S590-S590

Author(s):

Lorena Guerrero-Torres ◽

Isaac Núñez-Saavedra ◽

Yanink Caro-Vega ◽

Brenda Crabtree-Ramírez

Keyword(s):

Emergency Department ◽

Time Series ◽

Hiv Testing ◽

Teaching Hospital ◽

Educational Intervention ◽

Temporal Trends ◽

Hiv Screening ◽

Hiv Diagnosis ◽

Post Intervention ◽

Hiv Test

Abstract Background Among 230,000 people living with HIV in Mexico, 24% are unaware of their diagnosis, and half of newly diagnosed individuals are diagnosed with advanced disease. Early diagnosis is the goal to mitigate HIV epidemic. Missed opportunities may reflect a lack of clinicians’ consideration of HIV screening as part of routine medical care. We assessed whether an educational intervention on residents was effective to 1) improve the knowledge on HIV screening; 2) increase the rate of HIV tests requested in the hospitalization floor (HF) and the emergency department (ED); and 3) increase HIV diagnosis in HF and ED. Methods Internal Medicine and Surgery residents at a teaching hospital were invited to participate. The intervention occurred in August 2018 and consisted in 2 sessions on HIV screening with an expert. A questionnaire was applied before (BQ) and after (AQ) the intervention, which included HIV screening indications and clinical cases. The Institutional Review Board approved this study. Written informed consent was obtained from all participants. BQ and AQ scores were compared with a paired t-test. To evaluate the effect on HIV test rate in the HF and ED, an interrupted time series analysis was performed. Daily rates of tests were obtained from September 2016 to August 2019 and plotted along time. Restricted cubic splines (RCS) were used to model temporal trends. HIV diagnosis in HF and ED pre- and post-intervention were compared with a Fisher’s exact test. A p< 0.05 was considered significant. Results Among 104 residents, 57 participated and completed both questionnaires. BQ score was 79/100 (SD±12) and AQ was 85/100 (SD±8), p< .004. Time series of HIV testing had apparent temporal trends (Fig 1). HIV test rate in the HF increased (7.3 vs 11.1 per 100 episodes) and decreased in the ED (2.6 vs 2.3 per 100 episodes). HIV diagnosis increased in the HF, from 0/1079 (0%) pre-intervention to 5/894 (0.6%) post-intervention (p< .018) (Table 1). Fig 1. HIV test rates. Gray area represents post-intervention period. Table 1. Description of episodes, HIV tests and rates pre- and post-intervention in the Emergency Department and Hospitalization Floor. Conclusion A feasible educational intervention improved residents’ knowledge on HIV screening, achieved maintenance of a constant rate of HIV testing in the HF and increased the number of HIV diagnosis in the HF. However, these results were not observed in the ED, where administrative barriers and work overload could hinder HIV screening. Disclosures All Authors: No reported disclosures

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Assessment of Rangeland Degradation in New Mexico Using Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND)

Remote Sensing ◽

10.3390/rs13091618 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1618

Author(s):

Melakeneh G. Gedefaw ◽

Hatim M. E. Geli ◽

Temesgen Alemayehu Abera

Keyword(s):

Time Series ◽

New Mexico ◽

Trend Analysis ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Rangeland Management ◽

Environmental Benefits ◽

Drought Severity ◽

Assessment Framework ◽

Rangeland Degradation

Rangelands provide significant socioeconomic and environmental benefits to humans. However, climate variability and anthropogenic drivers can negatively impact rangeland productivity. The main goal of this study was to investigate structural and productivity changes in rangeland ecosystems in New Mexico (NM), in the southwestern United States of America during the 1984–2015 period. This goal was achieved by applying the time series segmented residual trend analysis (TSS-RESTREND) method, using datasets of the normalized difference vegetation index (NDVI) from the Global Inventory Modeling and Mapping Studies and precipitation from Parameter elevation Regressions on Independent Slopes Model (PRISM), and developing an assessment framework. The results indicated that about 17.6% and 12.8% of NM experienced a decrease and an increase in productivity, respectively. More than half of the state (55.6%) had insignificant change productivity, 10.8% was classified as indeterminant, and 3.2% was considered as agriculture. A decrease in productivity was observed in 2.2%, 4.5%, and 1.7% of NM’s grassland, shrubland, and ever green forest land cover classes, respectively. Significant decrease in productivity was observed in the northeastern and southeastern quadrants of NM while significant increase was observed in northwestern, southwestern, and a small portion of the southeastern quadrants. The timing of detected breakpoints coincided with some of NM’s drought events as indicated by the self-calibrated Palmar Drought Severity Index as their number increased since 2000s following a similar increase in drought severity. Some breakpoints were concurrent with some fire events. The combination of these two types of disturbances can partly explain the emergence of breakpoints with degradation in productivity. Using the breakpoint assessment framework developed in this study, the observed degradation based on the TSS-RESTREND showed only 55% agreement with the Rangeland Productivity Monitoring Service (RPMS) data. There was an agreement between the TSS-RESTREND and RPMS on the occurrence of significant degradation in productivity over the grasslands and shrublands within the Arizona/NM Tablelands and in the Chihuahua Desert ecoregions, respectively. This assessment of NM’s vegetation productivity is critical to support the decision-making process for rangeland management; address challenges related to the sustainability of forage supply and livestock production; conserve the biodiversity of rangelands ecosystems; and increase their resilience. Future analysis should consider the effects of rising temperatures and drought on rangeland degradation and productivity.

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Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

Epidemiology and Infection ◽

10.1017/s095026881800362x ◽

2019 ◽

Vol 147 ◽

Cited By ~ 4

Author(s):

C. W. Tian ◽

H. Wang ◽

X. M. Luo

Keyword(s):

Time Series ◽

Seasonal Variation ◽

Moving Average ◽

Foot And Mouth Disease ◽

Mouth Disease ◽

Sarima Model ◽

Autoregressive Integrated Moving Average ◽

Annual Peak ◽

Time Series Modelling ◽

Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

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