Red tide time series forecasting by combining ARIMA and deep belief network

2017 ◽  
Vol 125 ◽  
pp. 39-52 ◽  
Author(s):  
Mengjiao Qin ◽  
Zhihang Li ◽  
Zhenhong Du
Keyword(s):  
Time Series ◽  
Red Tide ◽  
Time Series Forecasting ◽  
Deep Belief Network ◽  
Belief Network

scholarly journals Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecasting

2016 ◽  
pp. 30-37 ◽  
Author(s):  
Takaomi Hirata ◽  
Takashi Kuremoto ◽  
Masanao Obayashi ◽  
Shingo Mabu ◽  
Kunikazu Kobayashi
Keyword(s):  
Time Series ◽  
Reinforcement Learning ◽  
Time Series Forecasting ◽  
Deep Belief Network ◽  
Belief Network

Time series forecasting using a deep belief network with restricted Boltzmann machines

2014 ◽  
Vol 137 ◽  
pp. 47-56 ◽  
Author(s):  
Takashi Kuremoto ◽  
Shinsuke Kimura ◽  
Kunikazu Kobayashi ◽  
Masanao Obayashi
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
Deep Belief Network ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Belief Network

Deep belief network-based AR model for nonlinear time series forecasting

2019 ◽  
Vol 77 ◽  
pp. 605-621 ◽  
Author(s):  
Wenquan Xu ◽  
Hui Peng ◽  
Xiaoyong Zeng ◽  
Feng Zhou ◽  
Xiaoying Tian ◽  
...  
Keyword(s):  
Time Series ◽  
Nonlinear Time Series ◽  
Time Series Forecasting ◽  
Deep Belief Network ◽  
Ar Model ◽  
Belief Network

scholarly journals A Cycle Deep Belief Network Model for Multivariate Time Series Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Shuqin Wang ◽  
Gang Hua ◽  
Guosheng Hao ◽  
Chunli Xie
Keyword(s):  
Time Series ◽  
Network Model ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Deep Belief Network ◽  
Learning Ability ◽  
Series Data ◽  
Temporal Data ◽  
Belief Network ◽  
Data Objects

Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.

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