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2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting
Recent approaches to time series forecasting, especially forecasting spatiotemporal sequences, have leveraged the approximation power of deep neural networks to model the complexity of such sequences, specifically approaches that are based on recurrent neural networks. Still, as spatiotemporal seque...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435848/ https://www.ncbi.nlm.nih.gov/pubmed/32731537 http://dx.doi.org/10.3390/s20154195 |
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author | Halim, Calvin Janitra Kawamoto, Kazuhiko |
author_facet | Halim, Calvin Janitra Kawamoto, Kazuhiko |
author_sort | Halim, Calvin Janitra |
collection | PubMed |
description | Recent approaches to time series forecasting, especially forecasting spatiotemporal sequences, have leveraged the approximation power of deep neural networks to model the complexity of such sequences, specifically approaches that are based on recurrent neural networks. Still, as spatiotemporal sequences that arise in the real world are noisy and chaotic, modeling approaches that utilize probabilistic temporal models, such as deep Markov models (DMMs), are favorable because of their ability to model uncertainty, increasing their robustness to noise. However, approaches based on DMMs do not maintain the spatial characteristics of spatiotemporal sequences, with most of the approaches converting the observed input into 1D data halfway through the model. To solve this, we propose a model that retains the spatial aspect of the target sequence with a DMM that consists of 2D convolutional neural networks. We then show the robustness of our method to data with large variance compared with naive forecast, vanilla DMM, and convolutional long short-term memory (LSTM) using synthetic data, even outperforming the DNN models over a longer forecast period. We also point out the limitations of our model when forecasting real-world precipitation data and the possible future work that can be done to address these limitations, along with additional future research potential. |
format | Online Article Text |
id | pubmed-7435848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74358482020-08-25 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting Halim, Calvin Janitra Kawamoto, Kazuhiko Sensors (Basel) Article Recent approaches to time series forecasting, especially forecasting spatiotemporal sequences, have leveraged the approximation power of deep neural networks to model the complexity of such sequences, specifically approaches that are based on recurrent neural networks. Still, as spatiotemporal sequences that arise in the real world are noisy and chaotic, modeling approaches that utilize probabilistic temporal models, such as deep Markov models (DMMs), are favorable because of their ability to model uncertainty, increasing their robustness to noise. However, approaches based on DMMs do not maintain the spatial characteristics of spatiotemporal sequences, with most of the approaches converting the observed input into 1D data halfway through the model. To solve this, we propose a model that retains the spatial aspect of the target sequence with a DMM that consists of 2D convolutional neural networks. We then show the robustness of our method to data with large variance compared with naive forecast, vanilla DMM, and convolutional long short-term memory (LSTM) using synthetic data, even outperforming the DNN models over a longer forecast period. We also point out the limitations of our model when forecasting real-world precipitation data and the possible future work that can be done to address these limitations, along with additional future research potential. MDPI 2020-07-28 /pmc/articles/PMC7435848/ /pubmed/32731537 http://dx.doi.org/10.3390/s20154195 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Halim, Calvin Janitra Kawamoto, Kazuhiko 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting |
title | 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting |
title_full | 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting |
title_fullStr | 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting |
title_full_unstemmed | 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting |
title_short | 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting |
title_sort | 2d convolutional neural markov models for spatiotemporal sequence forecasting |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435848/ https://www.ncbi.nlm.nih.gov/pubmed/32731537 http://dx.doi.org/10.3390/s20154195 |
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