深度学习与时间序列 Others 2015

2015

• Afan Galih Salman; Bayu Kanigoro; Yaya Heryadi (2015). Weather forecasting using deep learning techniques. Advanced Computer Science and Information Systems (ICACSIS), 2015 International Conference on.

  Summary: This study investigates deep learning techniques for weather forecasting. In particular, this study will compare prediction performance of Recurrence Neural Network (RNN), Conditional Restricted Boltzmann Machine (CRBM), and Convolutional Network (CN) models. Those models are tested using weather dataset which are collected from a number of weather stations.

  Type: Recurrent neural network, convolutional neural network

• Mladen Dalto (2015). Deep neural networks for time series prediction with applications in ultra-short-term wind forecasting

  Summary: The aim of this paper is to present deep neural network architectures and algorithms and explore their use in time series prediction. Shallow and deep neural networks coupled with two input variable selection algorithms are compared on a ultra-short-term wind prediction task.

  Type: MultiLayer Perceptron.