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Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation
[article]
2023
arXiv
pre-print
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This paper proposes a novel Spatio-Temporal Denoising Graph Autoencoder (STD-GAE) framework to impute missing PV Power Data. ...
The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and long-term reliability assessment of PV ...
Compared to STGNNs [41] , we specify an expressive sandwiched Spatio-temporal block, exploit data augmentation to improve the input quality, and support congurable data corruption to cope with diversied ...
arXiv:2302.10860v1
fatcat:36rqh6istnhmbfeyred42iavlq
SocialWave
2017
ACM Transactions on Intelligent Systems and Technology
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Furthermore, SocialWave, a visual analytic system, is developed to support both spatial and temporal investigative tasks. ...
Temporal dimension is also taken into account to help detect recency effect, and ground-truth data is integrated into the model to help measure the diffusion power. ...
-We design and develop SocialWave, a visual analytics system for interactive visual exploration and summarization of the complex spatio-temporal diffusion of information on social media. ...
doi:10.1145/3106775
fatcat:7nh6gxokebbj7kprq73mw3kmzy
A Data Imputation Model based on an Ensemble Scheme
[article]
2020
arXiv
pre-print
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In this paper, we propose an ensemble based approach for data imputation that takes into consideration the spatio-temporal aspect of the collected data and the reporting devices. ...
Various services can be provided at the EC for the immediate management of the collected data. One significant task is the management of missing values. ...
In this paper, we propose a data imputation technique to be adopted at edge nodes. ...
arXiv:2007.12860v1
fatcat:p2hcxivykrdcxkxdqng4hkisqy
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2310.10196v1 fatcat:3cer7yufdjfv3dgu5jteveb76e
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model ...
We primarily categorize the existing literature into two major clusters: large models for time series analysis (LM4TS) and spatio-temporal data mining (LM4STD). ...
LARGE MODELS FOR SPATIO-TEMPORAL DATA In this section, we examine the advancements of large models in spatio-temporal data mining across three primary data types: spatio-temporal graphs, temporal knowledge ...
arXiv:2310.10196v2
fatcat:rmgmzclzonfhnec2ed2bck5dwq
On the way toward systems biology of Aspergillus fumigatus infection
2011
International Journal of Medical Microbiology
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Recently, first steps were done to extend the integrative data analysis and computational modeling by evaluating spatio-temporal data (movies) that monitor interactions of A. fumigatus morphotypes (e.g ...
These data are analyzed for the elucidation of virulence determinants. The data analysis workflow starts with pre-processing including imputing of missing values and normalization. ...
Acknowledgements This work was supported by the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1160 'Colonisation and infection by human-pathogenic fungi'. ...
doi:10.1016/j.ijmm.2011.04.014
pmid:21555243
fatcat:4aoxdyjprfcu5cu56h74fqo53u
A Comprehensive Survey on Imputation of Missing Data in Internet of Things
2022
ACM Computing Surveys
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We believe this survey will provide a better understanding of the research of incomplete data and serve as a guide for future research. ...
Hence, this survey attempts to provide a structured and comprehensive overview of the research on the imputation of incomplete data in IoT. ...
The model was able to handle multisource data (spectral, spatial, and temporal) containing missing values. Generally, CNN imputation is used in Spatio-temporal image datasets. ...
doi:10.1145/3533381
fatcat:d3bebxqpyvbqhhekculoesd2ei
Geosmartness for Personalized and Sustainable Future Urban Mobility
[chapter]
2021
The Urban Book Series
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spatio-temporal analyses of mobility patterns as well as to investigate people's mobile decision making. ...
The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well ...
Multiple personalized graphs were utilized to model human mobility behavior and to embed a large variety of spatio-temporal information and structure in the graphs' weights and connections. ...
doi:10.1007/978-981-15-8983-6_6
fatcat:ggl5g7lvqfdhfir55loemdaox4
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2307.03759v1 fatcat:y6lwadqdnbhpjj4qtwbpldwu7y
Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. ...
imputation. ...
The former is meant to predict single future observations of the time series once at a time, i.e., the target at time t is Y := X t+H for H ∈ N steps ahead, while the latter makes predictions for a time ...
arXiv:2307.03759v2
fatcat:q2ac66op7zcgzj27gjekf4cpgu
Error Sources in the Analysis of Crowdsourced Spatial Tracking Data
2019
2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
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The propagation channels are presented schematically and a new approach to quantify the spatial data quality at the end of each processing stage is proposed. ...
This procedure, within the context of Smart Cities, ensures that the data analytics and resulting changes in policy are sufficiently substantiated by credible and reliable information. ...
Spatio-temporal measurement sequences For spatio-temporal measurement sequences in GNSS tracking data, we calculate seven metrics. ...
doi:10.1109/percomw.2019.8730710
dblp:conf/percom/GheluweLG19
fatcat:h3jhnof3lfb4dhvjmam7sc5fti
Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting
2019
IEEE Transactions on Knowledge and Data Engineering
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We assume that the city has deployed high-fidelity sensors for speed reading in a subset of edges; and the objective is to infer the speed readings for the remaining edges in the network; and to estimate ...
Extensive numerical experiments with real traffic data from the cities of Doha (Qatar) and Aarhus (Denmark) demonstrate that the proposed approach is appropriate for imputing the missing data and predicting ...
ACKNOWLEDGMENTS The authors would like to acknowledge and thank Dr. Fethi Filali from the Qatar Mobility Innovation Center for providing the data for Doha. ...
doi:10.1109/tkde.2019.2954868
fatcat:4w4qs2iaafbrxfxr56xufimste
Data-Driven Sustainability: Leveraging Big Data and Machine Learning to Build a Greener Future
2023
Babylonian Journal of Artificial Intelligence
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of supervised algorithms for prediction and unsupervised techniques for pattern discovery tailored to sustainability objectives; (iv) cloud-based model operationalization. ...
Advanced information technologies offer immense potential for confronting these issues via data-driven intelligence. ...
Acknowledgment The author extends gratitude to the institution for fostering a collaborative atmosphere that enhanced the quality of this research. ...
doi:10.58496/bjai/2023/005
fatcat:vto4hk4upbgujkiqylvvyvrupm
Methods for Analysis of Spatio-Temporal Bluetooth Tracking Data
2014
The Journal of urban technology
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As a next step to support analysts, we propose clustering the spatio-temporal presence and flow situations. ...
Deriving a number of objects for the spatiotemporal compartments and transitions among them gives interesting insights on the spatio-temporal behavior of moving objects. ...
Acknowledgments We thank Iulian Peca and Hermann Streich for support in the data collection phase. We thank the anonymous reviewers for their comments and suggestions. ...
doi:10.1080/10630732.2014.888215
fatcat:7pc5lvof5baozakqtreaokelhm
Development and application of statistical methodology for analysis of the phenomenon of multi‐drug resistance in the EU: demonstration of analytical approaches using antimicrobial resistance isolate‐based data
2016
EFSA Supporting Publications
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For illustration purposes, these methods are applied to a subset of the AMR data using an application developed with the R package "shiny". ...
Isolate-based data within the EU have been routinely collected since 2010 and reported to EFSA on a yearly basis. ...
Spatio-Temporal Models 3.1.6.1
Univariate Analysis -Country-level A univariate spatio-temporal model was fitted to the E.coli data for broilers at the country, NUTS-1, NUTS-2 and NUTS-3 level. ...
doi:10.2903/sp.efsa.2016.en-1084
fatcat:h5czgcpwnbb5jd476zrvpzmpva
Editor's Introduction: Quantitative Approaches to the Study of Terrorism
2011
Journal of quantitative criminology
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Although criminological research on terrorism has expanded dramatically since the 1970s, it has generally not been noteworthy as an area that has generated cutting edge methodological and statistical innovations ...
But as the contributions to this special issue of the JQC illustrate, the situation with regard to quantitative approaches to the study of terrorism has begun to rapidly change. ...
The researchers find strong support for the conclusion that many of the terrorist attacks attributed to these two distinctive groups were part of violent microcycles and that the spatio-temporal attack ...
doi:10.1007/s10940-011-9159-1
fatcat:dm6etsn4dvd4bnh5svunwq6vo4
Scanning the Issue
2020
IEEE transactions on intelligent transportation systems (Print)
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The GPS data were collected from about 36 000 taxi vehicles in Beijing city at 30-s intervals for six months. ...
A Mixed Path Size Logit (MPSL) model is proposed to analyze route choice behaviors in the process of taxi customersearching through considering spatio-temporal features of the route, including customer ...
Scanning the Issue
A Mixed Path Size Logit-Based Taxi Customer-Search Model Considering Spatio-Temporal Factors in Route Choice J. Tang, Y. Wang, W. Hao, F. Liu, H. Huang, and Y. ...
doi:10.1109/tits.2020.2980087
fatcat:zncxp7cluzgybbw3zjx4nxefti
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