scholarly journals Time series analysis to predict link quality of wireless community networks

2015 ◽  
Vol 93 ◽  
pp. 342-358 ◽  
Author(s):  
Pere Millan ◽  
Carlos Molina ◽  
Esunly Medina ◽  
Davide Vega ◽  
Roc Meseguer ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Link Quality ◽  
Community Networks ◽  
Series Analysis

scholarly journals COVID-19 and regional differences in the timeliness of hip-fracture surgery: an interrupted time-series analysis

2021 ◽  
Author(s):  
Davide Golinelli ◽  
Jacopo Lenzi ◽  
Emanuele Adorno ◽  
Maria Michela Gianino ◽  
Maria Pia Fantini
Keyword(s):  
Time Series ◽  
Quality Of Care ◽  
Hip Fracture ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Healthcare Services ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
The Impact

Background. It is of great importance to examine the impact of the healthcare reorganization adopted to confront the COVID19 pandemic on the quality of care provided by healthcare systems to non COVID 19 patients. The aim of this study is to assess the impact of the COVID19 national lockdown (March 9, 2020) on the quality of care provided to patients with hip fracture (HF) in Piedmont and Emilia-Romagna, 2 large regions of northern Italy severely hit by the pandemic. Methods. We calculated the percentage of HF patients undergoing surgery within 2 days of hospital admission. An interrupted time-series analysis was performed on weekly data from December 11, 2019 to June 9, 2020 (6 months), interrupting the series in the 2nd week of March. The same data observed the year before were included as a control time series with no intervention (lockdown) in the middle of the observation period. Results. Before the lockdown, 2day surgery was 69.9% in Piedmont and 79.2% in Emilia-Romagna; after the lockdown, these proportions were equal to 69.8% (-0.1%) and 69.3% (-9.9%), respectively. While Piedmont did not experience any drop in the amount of surgery, Emilia-Romagna exhibited a significantly decline at a weekly rate of -1.29% (95% CI = -1.71 to -0.88). Divergent trend patterns in the 2 study regions reflect local differences in pandemic timing as well as in healthcare services capacity, management, and emergency preparedness.

scholarly journals Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 578 ◽  
Author(s):  
Pere Millan ◽  
Carles Aliagas ◽  
Carlos Molina ◽  
Emmanouil Dimogerontakis ◽  
Roc Meseguer
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Social Impact ◽  
Internet Access ◽  
Community Networks ◽  
Prediction Algorithm ◽  
Quality Prediction ◽  
Series Analysis ◽  
End To End ◽  
The Impact

Community Networks have been around us for decades being initially deployed in the USA and Europe. They were designed by individuals to provide open and free “do it yourself” Internet access to other individuals in the same community and geographic area. In recent years, they have evolved as a viable solution to provide Internet access in developing countries and rural areas. Their social impact is measurable, as the community is provided with the right and opportunity of communication. Community networks combine wired and wireless links, and the nature of the wireless medium is unreliable. This poses several challenges to the routing protocol. For instance, Link-State routing protocols deal with End-to-End Quality tracking to select paths that maximize the delivery rate and minimize traffic congestion. In this work, we focused on End-to-End Quality prediction by means of time-series analysis to foresee which paths are more likely to change their quality. We show that it is possible to accurately predict End-to-End Quality with a small Mean Absolute Error in the routing layer of large-scale, distributed, and decentralized networks. In particular, we analyzed the path ETX behavior and properties to better identify the best prediction algorithm. We also analyzed the End-to-End Quality prediction accuracy some steps ahead in the future, as well as its dependency on the hour of the day. Besides, we quantified the computational cost of the prediction. Finally, we evaluated the impact of the usage for routing of our approach versus a simplified OLSR (ETX + Dijkstra) on an overloaded network.

scholarly journals STUDY OF THE INFLUENCE OF CHANGING THE PARAMETERS OF THE ARIMA MODEL ON THE QUALITY OF THE FORECAST FOR SHORT DATA SETS

2021 ◽  
Vol 21 (3) ◽  
pp. 36-46
Author(s):  
M.N. Fel’ker ◽  
◽  
V.V. Chesnov
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Arima Model ◽  
Annual Data ◽  
Data Sets ◽  
Arima Models ◽  
Data Set ◽  
Series Analysis ◽  
The Future

Time series, i.e. data collected at various times. The data collection segments may differ de-pending on the task. Time series are used for decision making. Time series analysis allows you to get some result that will determine the format of the decision. Time series analysis was carried out in very ancient times, for example, various calendars became a consequence of the analysis. Later, time series analysis was applied to study and forecast economic, social and other systems. Time se-ries appeared a long time ago. Once upon a time, ancient Babylonian astronomers, studying the po-sition of the stars, discovered the frequency of eclipses, which allowed them to predict their appearance in the future. Later, the analysis of time series, in a similar way, led to the creation of various calen-dars, for example, harvest calendars. In the future, in addition to natural areas, social and economic ones were added. Aim. Search for classification patterns of time series, allowing to understand whether it is possible to apply the ARIMA model for their short-term (3 counts) forecast. Materials and methods. Special software with ARIMA implementation and all need services is made. We examined 59 data sets with a short length and step equal a year, less than 20 values in the paper. The data was processed using Python libraries: Statsmodels and Pandas. The Dickey – Fuller test was used to de-termine the stationarity of the series. The stationarity of the time series allows for better forecasting. The Akaike information criterion was used to select the best model. Recommendations for a rea-sonable selection of parameters for adjusting ARIMA models are obtained. The dependence of the settings on the category of annual data set is shown. Conclusion. After processing the data, four categories (patterns) of year data sets were identified. Depending on the category ranges of parame-ters were selected for tuning ARIMA models. The suggested ranges will allow to determine the starting parameters for exploring similar datasets. Recommendations for improving the quality of post-forecast and forecast using the ARIMA model by adjusting the settings are given.

scholarly journals Does Monetary Support Increase the Number of Scientific Papers? An Interrupted Time Series Analysis

2018 ◽  
Vol 3 (1) ◽  
pp. 19-39 ◽  
Author(s):  
Yaşar Tonta
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Support Program ◽  
Series Data ◽  
Control Group ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
The Impact

Abstract Purpose One of the main indicators of scientific production is the number of papers published in scholarly journals. Turkey ranks 18th place in the world based on the number of scholarly publications. The objective of this paper is to find out if the monetary support program initiated in 1993 by the Turkish Scientific and Technological Research Council (TÜBİTAK) to incentivize researchers and increase the number, impact, and quality of international publications has been effective in doing so. Design/methodology/approach We analyzed some 390,000 publications with Turkish affiliations listed in the Web of Science (WoS) database between 1976 and 2015 along with about 157,000 supported ones between 1997 and 2015. We used the interrupted time series (ITS) analysis technique (also known as “quasi-experimental time series analysis” or “intervention analysis”) to test if TÜBİTAK’s support program helped increase the number of publications. We defined ARIMA (1,1,0) model for ITS data and observed the impact of TÜBİTAK’s support program in 1994, 1997, and 2003 (after one, four and 10 years of its start, respectively). The majority of publications (93%) were full papers (articles), which were used as the experimental group while other types of contributions functioned as the control group. We also carried out a multiple regression analysis. Findings TÜBİTAK’s support program has had negligible effect on the increase of the number of papers with Turkish affiliations. Yet, the number of other types of contributions continued to increase even though they were not well supported, suggesting that TÜBİTAK’s support program is probably not the main factor causing the increase in the number of papers with Turkish affiliations. Research limitations Interrupted time series analysis shows if the “intervention” has had any significant effect on the dependent variable but it does not explain what caused the increase in the number of papers if it was not the intervention. Moreover, except the “intervention”, other “event(s)” that might affect the time series data (e.g., increase in the number of research personnel over the years) should not occur during the period of analysis, a prerequisite that is beyond the control of the researcher. Practical implications TÜBİTAK’s “cash-for-publication” program did not seem to have direct impact on the increase of the number of papers published by Turkish authors, suggesting that small amounts of payments are not much of an incentive for authors to publish more. It might perhaps be a better strategy to concentrate limited resources on a few high impact projects rather than to disperse them to thousands of authors as “micropayments.” Originality/value Based on 25 years’ worth of payments data, this is perhaps one of the first large-scale studies showing that “cash-for-publication” policies or “piece rates” paid to researchers tend to have little or no effect on the increase of researchers’ productivity. The main finding of this paper has some implications for countries wherein publication subsidies are used as an incentive to increase the number and quality of papers published in international journals. They should be prepared to consider reviewing their existing support programs (based usually on bibliometric measures such as journal impact factors) and revising their reward policies.

An extended cross-country time-series analysis of the volume and quality of consumption

1985 ◽  
Vol 18 (2-3) ◽  
pp. 117-120
Author(s):  
Henri Theil ◽  
Renate Finke ◽  
Mercedes C. Rosalsky
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis ◽  
Cross Country

Short-term link quality prediction using nonparametric time series analysis

2015 ◽  
Vol 58 (8) ◽  
pp. 1-15 ◽  
Author(s):  
LiNa Weng ◽  
Ping Zhang ◽  
ZhiYong Feng ◽  
HongWei Cheng ◽  
Hao Lian ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Link Quality ◽  
Quality Prediction ◽  
Short Term ◽  
Series Analysis ◽  
Link Quality Prediction

scholarly journals COVID-19 and regional differences in the timeliness of hip-fracture surgery: an interrupted time-series analysis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12046
Author(s):  
Davide Golinelli ◽  
Jacopo Lenzi ◽  
Emanuele Adorno ◽  
Maria Michela Gianino ◽  
Maria Pia Fantini
Keyword(s):  
Time Series ◽  
Quality Of Care ◽  
Hip Fracture ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Healthcare Services ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
The Impact

Background It is of great importance to examine the impact of the healthcare reorganization adopted to confront the COVID-19 pandemic on the quality of care provided to non-COVID-19 patients. The aim of this study is to assess the impact of the COVID-19 national lockdown (March 9, 2020) on the quality of care provided to patients with hip fracture (HF) in Piedmont and Emilia-Romagna, two large regions of northern Italy severely hit by the pandemic. Methods We calculated the percentage of HF patients undergoing surgery within 2 days of hospital admission. An interrupted time-series analysis was performed on weekly data from December 11, 2019 to June 9, 2020 (≈6 months), interrupting the series in the 2nd week of March. The same data observed the year before were included as a control time series with no “intervention” (lockdown) in the middle of the observation period. Results Before the lockdown, 2-day surgery was 69.9% in Piedmont and 79.2% in Emilia-Romagna; after the lockdown, these proportions were equal to 69.8% (–0.1%) and 69.3% (–9.9%), respectively. While Piedmont did not experience any drop in the amount of surgery, Emilia-Romagna exhibited a significant decline at a weekly rate of –1.29% (95% CI [−1.71 to −0.88]). Divergent trend patterns in the two study regions reflect local differences in pandemic timing as well as in healthcare services capacity, management, and emergency preparedness.

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