深度学习与时间序列 Others 2016

2016

• Chao Yuan; Amit Chakraborty (2016). Deep Convolutional Factor Analyser for Multivariate Time Series Modeling. Data Mining (ICDM), 2016 IEEE 16th International Conference on.

  Summary: The paper presents a deep convolutional factor analyser (DCFA) for multivariate time series modeling. The network is constructed in a way that bottom layer nodes are independent. Through a process of up-sampling and convolution, higher layer nodes gain more temporal dependency.

  Type: Convolutional neural network

• Yuta Kaneko; Katsutoshi Yada (2016). A Deep Learning Approach for the Prediction of Retail Store Sales. Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on.

  Summary: The present study uses three years’ worth of point-of-sale (POS) data from a retail store to construct a sales prediction model that, given the sales of a particular day, predicts the changes in sales on the following day.

  Type: Not specified

• Tomah Sogabe; Haruhisa Ichikawa; Tomah Sogabe; Katsuyoshi Sakamoto; Koichi Yamaguchi; Masaru Sogabe; Takashi Sato; Yusuke Suwa (2016). Optimization of decentralized renewable energy system by weather forecasting and deep machine learning techniques. Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2016 IEEE.

  Summary: This work reports on employing the deep learning artificial intelligence techniques to predict the energy consumption and power generation together with the weather forecasting numerical simulation. An optimization tool platform using Boltzmann machine algorithm for NMIP problem is also proposed for better computing scalable decentralized renewable energy system.

  Type: a novel optimization tool platform using Boltzmann machine algorithm for NMIP