Skip to content

Deep Learning Methods for Spatio-Temporal Correlation Patterns - Overview

Note

This document has been machine translated.

Deep learning methods (CRNN, LGBM) can be applied to event data to learn and predict spatiotemporal correlation patterns.

Convolutional Recurrent Neural Network (CRNN) method integrates Convolutional Neural Network (CNN) learning of spatial patterns and Long Short Term Memory (LSTM) learning of temporal patterns for environmental data such as weather and atmosphere. Neural Network (CRNN) method, which integrates spatial pattern learning with Convolutional Neural Network (CNN) and temporal pattern learning with Long Short Term Memory (LSTM), to learn and predict spatiotemporal correlation patterns.

It can also learn and predict time-varying models of environmental data using LightGBM.

For more information on CRNN, please refer to the following paper.

  • Zhao, P. and Zettsu, K.: Convolution Recurrent Neural Networks for Short-Term Prediction of Atmospheric Sensing Data, The 4th IEEE International Conference on Smart Data (SmartData 2018), Halifax, Canada, pp.815-821 (July 2018).

Endpoint

https://<api-server-endpoint>/api/v1/provenance/jsonrpc