Next Article in Journal
State and Force Estimation on a Rotating Helicopter Blade through a Kalman-Based Approach
Previous Article in Journal
Smart Sensor Based on Biofeedback to Measure Child Relaxation in Out-of-Home Care
Previous Article in Special Issue
Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments
Open AccessArticle

2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting

1
Department of Applied and Cognitive Informatics, Graduate School of Science and Engineering, Chiba University, Chiba-shi, Chiba 263-8522, Japan
2
Graduate School of Engineering, Chiba University, Chiba-shi, Chiba 263-8522, Japan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4195; https://doi.org/10.3390/s20154195
Received: 29 June 2020 / Revised: 22 July 2020 / Accepted: 26 July 2020 / Published: 28 July 2020
(This article belongs to the Special Issue Deep Learning-Based Soft Sensors)
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. View Full-Text
Keywords: spatiotemporal forecasting; time series prediction; deep neural networks; deep Markov model; CNN; LSTM; DMM spatiotemporal forecasting; time series prediction; deep neural networks; deep Markov model; CNN; LSTM; DMM
Show Figures

Figure 1

MDPI and ACS Style

Halim, C.J.; Kawamoto, K. 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting. Sensors 2020, 20, 4195.

AMA Style

Halim CJ, Kawamoto K. 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting. Sensors. 2020; 20(15):4195.

Chicago/Turabian Style

Halim, Calvin J.; Kawamoto, Kazuhiko. 2020. "2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting" Sensors 20, no. 15: 4195.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop