scholarly journals COVIDSpread: real-time prediction of COVID-19 spread based on time-series modelling

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1110
Author(s):  
Siroos Shahriari ◽  
Taha Hossein Rashidi ◽  
AKM Azad ◽  
Fatemeh Vafaee
Keyword(s):  
Time Series ◽  
Real Time ◽  
Time Window ◽  
Time Series Models ◽  
Data Streaming ◽  
Time Data ◽  
Time Prediction ◽  
Time Series Modelling ◽  
Short Run ◽  
Modelling Techniques

A substantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with modelling techniques to provide real-time insights. This study introduces a unified platform, COVIDSpread, which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform uses time series models to capture any possible non-linearity in the data. COVIDSpread enables lay users, and experts, to examine the data and develop several customized models with different restrictions such as models developed for a specific time window of the data. COVIDSpread is available here: http://vafaeelab.com/COVID19TS.html.

scholarly journals Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment

2020 ◽  
Author(s):  
Taha Hossein Rashidi ◽  
Siroos Shahriari ◽  
AKM Azad ◽  
Fatemeh Vafaee
Keyword(s):  
Time Series ◽  
Real Time ◽  
Time Window ◽  
Data Streaming ◽  
Time Data ◽  
Social Distancing ◽  
Time Series Modelling ◽  
Short Run ◽  
Modelling Techniques ◽  
Effective Policy

AbstractSubstantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with advanced modelling techniques to provide real-time insights. This study introduces a unified platform which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform is backed up by advanced time series models to capture any possible non-linearity in the data which is enhanced by the capability of measuring the expected impact of preventive interventions such as social distancing and lockdowns. The platform enables lay users, and experts, to examine the data and develop several customized models with different restriction such as models developed for specific time window of the data. Our policy assessment of the case of Australia, shows that social distancing and travel ban restriction significantly affect the reduction of number of cases, as an effective policy.

scholarly journals Air Pollution Prediction Using an Ensemble of Dynamic Transfer Models for Multivariate Time Series

2021 ◽  
Vol 13 (3) ◽  
pp. 1367
Author(s):  
Taewoon Kong ◽  
Dongguen Choi ◽  
Geonseok Lee ◽  
Kichun Lee
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Real Time ◽  
Multivariate Time Series ◽  
Time Data ◽  
Time Prediction ◽  
Real Time Data ◽  
Air Pollution Prediction ◽  
Pollution Prediction

Entering a new era of big data, analysis of large amounts of real-time data is important, and air quality data as streaming time series are measured by several different sensors. To this end, numerous methods for time-series forecasting and deep-learning approaches based on neural networks have been used. However, they usually rely on a certain model with a stationary condition, and there are few studies of real-time prediction of dynamic massive multivariate data. Use of a variety of independent variables included in the data is important to improve forecasting performance. In this paper, we proposed a real-time prediction approach based on an ensemble method for multivariate time-series data. The suggested method can select multivariate time-series variables and incorporate real-time updatable autoregressive models in terms of performance. We verified the proposed model using simulated data and applied it to predict air quality measured by five sensors and failures based on real-time performance log data in server systems. We found that the proposed method for air pollution prediction showed effective and stable performance for both short- and long-term prediction tasks. In addition, traditional methods for abnormality detection have focused on present status of objects as either normal or abnormal based on provided data, we protectively predict expected statuses of objects with provided real-time data and implement effective system management in cloud environments through the proposed method.

scholarly journals Target tracking accuracy and latency with different 4D ultrasound systems – a robotic phantom study

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Svenja Ipsen ◽  
Sven Böttger ◽  
Holger Schwegmann ◽  
Floris Ernst
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Template Matching ◽  
Image Data ◽  
Distinct Advantage ◽  
Reference Trajectory ◽  
Data Streaming ◽  
Tracking Accuracy ◽  
Time Data ◽  
4D Ultrasound

AbstractUltrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

Real-Time Data Streaming Architecture and Intelligent Workflow Management for Processing Massive Ecological Videos

2013 ◽  
Author(s):  
Gayathri Nadarajan ◽  
Cheng-Lin Yang ◽  
Yun-Heh Chen-Burger ◽  
Yu-Jung Cheng ◽  
Sun-In Lin ◽  
...  
Keyword(s):  
Real Time ◽  
Workflow Management ◽  
Data Streaming ◽  
Time Data ◽  
Real Time Data

scholarly journals Monitoring distributed computing beyond the traditional time-series histogram

2020 ◽  
Vol 245 ◽  
pp. 03036
Author(s):  
M S Doidge ◽  
P. A. Love ◽  
J Thornton
Keyword(s):  
Time Series ◽  
Distributed Computing ◽  
Real Time ◽  
A Priori ◽  
Time Data ◽  
Use Of Time ◽  
Computing Services ◽  
Monitoring Tools ◽  
Novel Approach ◽  
Current Monitoring

In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.

scholarly journals Quarterly Regional GDP Flash Estimates by Means of Benchmarking and Chain Linking

2015 ◽  
Vol 31 (4) ◽  
pp. 627-647 ◽  
Author(s):  
Ángel Cuevas ◽  
Enrique M. Quilis ◽  
Antoni Espasa
Keyword(s):  
Time Series ◽  
Business Cycles ◽  
Real Time ◽  
Time Series Models ◽  
National Accounts ◽  
Spanish Economy ◽  
Short Term ◽  
Quarterly Data ◽  
New Instrument ◽  
Term Monitoring

Abstract In this article we propose a methodology for estimating the GDP of a country’s different regions, providing quarterly profiles for the annual official observed data. Thus the article offers a new instrument for short-term monitoring that allows the analysts to quantify the degree of synchronicity among regional business cycles. Technically, we combine time-series models with benchmarking methods to process short-term quarterly indicators and to estimate quarterly regional GDPs ensuring their temporal and transversal consistency with the National Accounts data. The methodology addresses the issue of nonadditivity, explicitly taking into account the transversal constraints imposed by the chain-linked volume indexes used by the National Accounts, and provides an efficient combination of structural as well as short-term information. The methodology is illustrated by an application to the Spanish economy, providing real-time quarterly GDP estimates, that is, with a minimum compilation delay with respect to the national quarterly GDP. The estimated quarterly data are used to assess the existence of cycles shared among the Spanish regions.

scholarly journals Web Traffic Time Series Forecasting using ARIMA and LSTM RNN

2020 ◽  
Vol 32 ◽  
pp. 03017
Author(s):  
Tejas Shelatkar ◽  
Stephen Tondale ◽  
Swaraj Yadav ◽  
Sheetal Ahir
Keyword(s):  
Time Series ◽  
Real Time ◽  
Time Series Forecasting ◽  
Web Traffic ◽  
Time Data ◽  
Traffic Forecasting ◽  
Traffic Time Series ◽  
Breakdown Time ◽  
Server Breakdown ◽  
Multiple Domains

Nowadays, web traffic forecasting is a major problem as this can cause setbacks to the workings of major websites. Time-series forecasting has been a hot topic for research. Predicting future time series values is one of the most difficult problems in the industry. The time series field encompasses many different issues, ranging from inference and analysis to forecasting and classification. Forecasting the network traffic and displaying it in a dashboard that updates in real-time would be the most efficient way to convey the information. Creating a Dashboard would help in monitoring and analyzing real-time data. In this day and age, we are too dependent on Google server but if we want to host a server for large users we could have predicted the number of users from previous years to avoid server breakdown. Time Series forecasting is crucial to multiple domains. ARIMA; LSTM RNN; web traffic; prediction;time series;

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