Deep Models for Spatiotemporal 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 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.
Calvin Janitra Halim and Kazuhiko Kawamoto, 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting, Sensors 20, no. 15: 4195, 2020.
Calvin Janitra Halim and Kazuhiko Kawamoto, Deep Markov Models for Data Assimilation in Chaotic Dynamical Systems, Advances in Artificial Intelligence, Vol. 1128, pp. 37-44, Springer International Publishing, 2020.
Katsuya Kosukegawa, Yasukuni Mori, Hiroki Suyari, Kazuhiko Kawamoto, Spatiotemporal forecasting of track geometry irregularities with exogenous factors, arXiv:2211.03549, 2022.