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      2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting

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          Abstract

          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.

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          Deep residual learning for image recognition

           K HE,  X. ZHANG,  S. REN (2020)
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            Unpaired image-to-image translation using cycle-consistent adversarial networks

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              Convolutional LSTM network: A machine learning approach for precipitation nowcasting

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                28 July 2020
                August 2020
                : 20
                : 15
                Affiliations
                [1 ]Department of Applied and Cognitive Informatics, Graduate School of Science and Engineering, Chiba University, Chiba-shi, Chiba 263-8522, Japan; calvinjh@ 123456chiba-u.jp
                [2 ]Graduate School of Engineering, Chiba University, Chiba-shi, Chiba 263-8522, Japan
                Author notes
                Article
                sensors-20-04195
                10.3390/s20154195
                7435848
                32731537
                © 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/).

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