scholarly journals Tapis v3 Streams API: Time‐series and data‐driven event support in science gateway infrastructure

2020 ◽  
Author(s):  
Sean B. Cleveland ◽  
Anagha Jamthe ◽  
Smruti Padhy ◽  
Joe Stubbs ◽  
Steven Terry ◽  
...  
Keyword(s):  
Time Series ◽  
Data Driven ◽  
Science Gateway

Neural Data-Driven Captioning of Time-Series Line Charts

2020 ◽  
Author(s):  
Andrea Spreafico ◽  
Giuseppe Carenini
Keyword(s):  
Time Series ◽  
Data Driven ◽  
Neural Data

Data-driven prediction method for characteristics of voltage sag based on fuzzy time series

2022 ◽  
Vol 134 ◽  
pp. 107394
Author(s):  
Ying Wang ◽  
Min-hui Yang ◽  
Hua-ying Zhang ◽  
Xian Wu ◽  
Wen-xi Hu
Keyword(s):  
Time Series ◽  
Prediction Method ◽  
Fuzzy Time Series ◽  
Data Driven ◽  
Voltage Sag

Closure to “Time-Series Prediction of Streamflows of Malaysian Rivers Using Data-Driven Techniques” by Siraj Muhammed Pandhiani, Parveen Sihag, Ani Bin Shabri, Balraj Singh, and Quoc Bao Pham

2021 ◽  
Vol 147 (9) ◽  
pp. 07021015
Author(s):  
Siraj Muhammed Pandhiani ◽  
Parveen Sihag ◽  
Ani Bin Shabri ◽  
Balraj Singh ◽  
Quoc Bao Pham
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Data Driven ◽  
Using Data

scholarly journals Multivariate Time Series for Data-Driven Endpoint Prediction in the Basic Oxygen Furnace

2018 ◽  
Author(s):  
Davi Alberto Sala ◽  
Azarakhsh Jalalvand ◽  
Andy Van Yperen-De Deyne ◽  
Erik Mannens
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Basic Oxygen Furnace ◽  
Data Driven ◽  
Basic Oxygen ◽  
Endpoint Prediction

scholarly journals Data-driven filtration and segmentation of mesoscale neural dynamics

2021 ◽  
Author(s):  
Sydney C. Weiser ◽  
Brian R. Mullen ◽  
Desiderio Ascencio ◽  
James B. Ackman
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Background Noise ◽  
Activity Patterns ◽  
Data Driven ◽  
Neural Dynamics ◽  
Cortical Surface ◽  
Cerebral Function ◽  
Spatial Segmentation ◽  
Video Recordings

Recording neuronal group activity across the cortical hemispheres from awake, behaving mice is essential for understanding information flow across cerebral networks. Video recordings of cerebral function comes with challenges, including optical and movement-associated vessel artifacts, and limited references for time series extraction. Here we present a data-driven workflow that isolates artifacts from calcium activity patterns, and segments independent functional units across the cortical surface. Independent Component Analysis utilizes the statistical interdependence of pixel activation to completely unmix signals from background noise, given sufficient spatial and temporal samples. We also utilize isolated signal components to produce segmentations of the cortical surface, unique to each individual’s functional patterning. Time series extraction from these maps maximally represent the underlying signal in a highly compressed format. These improved techniques for data pre-processing, spatial segmentation, and time series extraction result in optimal signals for further analysis.

scholarly journals Data-driven Time Series Based Prediction in Smart Home Appliance Energy Consumption

2019 ◽  
Vol 178 (15) ◽  
pp. 41-46
Author(s):  
Md. Taksir ◽  
Sharmin Aktar
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Smart Home ◽  
Data Driven ◽  
Home Appliance

scholarly journals Estimating infection-related human mobility networks based on time series data of COVID-19 infection in Japan

2021 ◽  
Author(s):  
Tetsuya Yamada ◽  
Shoi Shi
Keyword(s):  
Time Series ◽  
Infectious Diseases ◽  
Time Series Data ◽  
Human Mobility ◽  
Emerging Infectious Diseases ◽  
Human Movement ◽  
Data Driven ◽  
Disease Spread ◽  
Series Data ◽  
Data Infrastructure

Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in 21 century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in the countries that the traffic and human movement data infrastructure is not yet developed. In this study, we devised a method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and predicted the spread of infection using the Tokyo Olympics as a model. The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide.

Integrating Data-Driven Segmentation, Local Feature Extraction and Fisher Kernel Encoding to Improve Time Series Classification

2018 ◽  
Vol 49 (1) ◽  
pp. 43-66
Author(s):  
Weiping Huang ◽  
Boxuan Yue ◽  
Qinghua Chi ◽  
Jun Liang
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Local Feature ◽  
Data Driven ◽  
Time Series Classification ◽  
Fisher Kernel ◽  
Local Feature Extraction

Data-Driven Time Series Forecasting for Social Studies Using Spatio-Temporal Graph Neural Networks

2021 ◽  
Author(s):  
Yi-Fan Li ◽  
Bo Dong ◽  
Latifur Khan ◽  
Bhavani Thuraisingham ◽  
Patrick T. Brandt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Social Studies ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Graph Neural Networks
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