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Automatically Inferring Data Quality for Spatiotemporal Forecasting
2018
International Conference on Learning Representations
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In this paper, we propose a novel solution that can automatically infer data quality levels of different sources through local variations of spatiotemporal signals without explicit labels. ...
Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching ...
We thank Michael Tsang for discussion and his comments that greatly improved the manuscript. ...
dblp:conf/iclr/SeoMBL18
fatcat:riwjkdrp2nh65ihs3h2s3rjcty
Spatiotemporal deep learning model for citywide air pollution interpolation and prediction
[article]
2019
arXiv
pre-print
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Recently, air pollution is one of the most concerns for big cities. Predicting air quality for any regions and at any time is a critical requirement of urban citizens. ...
Specially, we introduce how to transform the air pollution data into sequences of images which leverages the using of ConvLSTM model to interpolate and predict air quality for the entire city at the same ...
If
the error is small then we can infer that other spatiotemporal
data has a remarkable effect on air pollution interpolation. ...
arXiv:1911.12919v1
fatcat:tjp3qmm3kbfnddny4s44wa3nhq
How events unfold
2016
SIGSPATIAL Special
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event forecasting. ...
There has been significant recent interest in the application of social media analytics for spatiotemporal event mining. However, no structured survey exists to capture developments in this space. ...
How to utilize social media data for forecasting is an active research topic of current interest. ...
doi:10.1145/2876480.2876485
fatcat:34dx66my65b4pobofkioeaqknm
Preface: special issue on geo-social media analytics
2018
Geoinformatica
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Exploiting the open source data in conjunction with their spatiotemporal contexts can enhance our understanding of the physical environment, societal condition, and the dynamic and complex relationships ...
For example, in the context of disaster response, Twitter feeds and Flikr imageries can provide a rich and valuable avenue for monitoring the spatial distribution of affected areas and population sentiments ...
addressing the challenges of highly skewed data availability and distributions, optimizing analytical models to handle continuously evolving data, inferring and extracting implicit and ambiguous spatiotemporal ...
doi:10.1007/s10707-018-0324-7
fatcat:j7gww2mcxrcofpbuoss5qm4z2u
Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction
[article]
2021
arXiv
pre-print
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We conduct a demonstration of DeepLATTE using publicly available data for an important public health topic, air quality prediction, in a well-studied, complex physical environment - Los Angeles. ...
This paper presents a novel deep learning architecture, Deep learning predictions for LocATion-dependent Time-sEries data (DeepLATTE), that explicitly incorporates theories of spatial statistics into neural ...
Finally, the refined spatiotemporal embeddings are fed to fully connected layers to infer air quality prediction for each cell.
E. ...
arXiv:2112.05313v1
fatcat:6hgnz3p32je2bmtko5og75xb7y
PM2.5 Prediction with a Novel Multi-Step-Ahead Forecasting Model Based on Dynamic Wind Field Distance
2019
International Journal of Environmental Research and Public Health
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A local stateful LSTM model was employed to capture temporal correlations in historical air quality and meteorological data for each related site. ...
Then, these temporal features were integrated as a spatiotemporal matrix, and input into CNN for extracting spatiotemporal correlation features. ...
Acknowledgments: We are very grateful for the comments on this paper from Teacher Steve and. Mao that significantly increase the clarity of this work. ...
doi:10.3390/ijerph16224482
fatcat:vvgv43lkibf7na4j5wdhl7ibwe
Poster Abstract: A Dynamic Data-Driven Prediction Model for Sparse Collaborative Sensing Applications
[article]
2019
arXiv
pre-print
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However, due to the resource constraints, collaborative sensing applications normally only collect measurements from a subset of physical locations and predict the measurements for the rest of locations ...
This poster presents a new data-driven solution for sparse collaborative sensing applications. ...
The dynamic spatial weight matrix W t i allows the model to infer unseen data from spatial neighbors. ...
arXiv:1909.04111v1
fatcat:wrdpn2ibojhmjoknxrceamog7y
On how to incorporate public sources of situational context in descriptive and predictive models of traffic data
2021
European Transport Research Review
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spatiotemporal traffic data structures: i) georeferenced time series data; ii) origin-destination tensor data; iii) raw traffic event data. ...
, triggering new opportunities for context-aware traffic data analysis. ...
Acknowledgements The authors thank the support of CARRIS, METRO and Câmara Municipal de Lisboa) (particularly, Gabinete de Mobilidade and Centro de Operações Integrado) for the data provision and valuable ...
doi:10.1186/s12544-021-00519-w
fatcat:u3y4vk33orbsjeualot2kqawjm
Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit
[article]
2020
arXiv
pre-print
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Model architecture includes four branches for inflow, outflow, graph-network topology, as well as weather conditions and air quality. ...
Three time granularities (10, 15, and 30 min) are chosen to conduct short-term passenger flow forecasting. ...
w represents 11 indicators for weather-condition and air-quality data. ...
arXiv:1912.12563v2
fatcat:f7xqbfv3zzgbhmiucobjhharau
A Deep Hybrid Model for Weather Forecasting
2015
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15
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We also derive an efficient learning and inference procedure that allows for large scale optimization of the model parameters. ...
We explore new directions with forecasting weather as a dataintensive challenge that involves inferences across space and time. ...
Acknowledgments We are grateful to Imke Durre for answering our queries concerning the IGRA dataset. ...
doi:10.1145/2783258.2783275
dblp:conf/kdd/GroverKH15
fatcat:62rqdnmt2va3jbcgoc7oeizn4a
Spatiotemporal Feature Learning Based Hour-Ahead Load Forecasting for Energy Internet
2020
Electronics
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In this paper, we analyze the characteristics of the load forecasting task in the Energy Internet context and the deficiencies of existing methods and then propose a data driven approach for one-hour-ahead ...
for multiple consecutive time points before the forecasted hour. ...
Consequently, for a given forecast zone, making full use of spatiotemporally related information will be beneficial for forecasting tasks. ...
doi:10.3390/electronics9010196
fatcat:tg7a25ya75cplhq3j7pyjhdg4e
An Ontology-Driven Approach for Integrating Intelligence to Manage Human and Ecological Health Risks in the Geospatial Sensor Web
2018
Sensors
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We demonstrate this intelligent system through a case study of automatic prediction of air quality and related health risk. ...
However, there are rarely specific geospatial sensor web (GSW) applications for certain ecological public health questions. ...
Special thanks to editors and reviewers for providing valuable insight into this article.
Conflicts of Interest: The authors declare that they have no conflicts of interest. ...
doi:10.3390/s18113619
pmid:30366399
fatcat:msokkn4o3rcklom7ryzig6wndq
Non-Intrusive Reduced Models based on Operator Inference for Chaotic Systems
[article]
2022
arXiv
pre-print
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This work explores the physics-driven machine learning technique Operator Inference (OpInf) for predicting the state of chaotic dynamical systems. ...
The quality of the OpInf predictions is assessed via the Normalized Root Mean Squared Error (NRMSE) metric from which the Valid Prediction Time (VPT) is computed. ...
Acknowledgments ACNJ acknowledges the Brazil's IBM Research laboratory for supporting this work. ...
arXiv:2206.01604v2
fatcat:57eksjhamvgnzmaf7o3ad2cddm
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods
2015
International Conference on Machine Learning
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We propose new scalable Kronecker methods for Gaussian processes with non-Gaussian likelihoods, using a Laplace approximation which involves linear conjugate gradients for inference, and a lower bound ...
Our approach has near linear scaling, requiring O(Dn D+1 D ) operations and O(Dn 2 D ) storage, for n training data-points on a dense D > 1 dimensional grid. ...
Our kernel learning automatically discovered multiscale seasonal trends and our inference generated highly accurate long-range forecasts, with accurate uncertainty intervals. instigated their Proceedings ...
dblp:conf/icml/FlaxmanWNNS15
fatcat:cygsk6bobvcjhfe57wxxwibgt4
A Multivariate Frequency-Domain Approach to Long-Lead Climatic Forecasting*
1998
Weather and forecasting
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, and an associated forecasting methodology is introduced. ...
This combined signal detection/forecasting scheme exhibits significantly greater skill than conventional forecasting approaches in the context of a synthetic example consistent with the adopted paradigm ...
Formal techniques for automatically detecting and correcting for such errors still need to be developed. ...
doi:10.1175/1520-0434(1998)013<0058:amfdat>2.0.co;2
fatcat:fqpcgdrgcvasnocruiht73jtzy
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