A Novel LSTM for Multivariate Time Series with Massive Missingness

Multivariate time series with missing data is ubiquitous when the streaming data is collected by sensors or any other recording instruments. For instance, the outdoor sensors gathering different meteorological variables may encounter low material sensitivity to specific situations, leading to incomplete information gathering. This is problematic in time series prediction with massive missingness and different missing rate of variables. Contribution addressing this problem on the regression task of meteorological datasets by employing Long Short-Term Memory (LSTM), capable of controlling the information flow with its memory unit, is still missing. In this paper, we propose a novel model called forward and backward variable-sensitive LSTM (FBVS-LSTM) consisting of two decay mechanisms and some informative data. The model inputs are mainly the missing indicator, time intervals of missingness in both forward and backward direction and missing rate of each variable. We employ this information to address the so-called missing not at random (MNAR) mechanism. Separately learning the features of each parameter, the model becomes adapted to deal with massive missingness. We conduct our experiment on three real-world datasets for the air pollution forecasting. The results demonstrate that our model performed well along with other LSTM-derivation models in terms of prediction accuracy.

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Correlational graph attention-based Long Short-Term Memory network for multivariate time series prediction

Applied Soft Computing ◽

10.1016/j.asoc.2021.107377 ◽

2021 ◽

pp. 107377

Author(s):

Shuang Han ◽

Hongbin Dong ◽

Xuyang Teng ◽

Xiaohui Li ◽

Xiaowei Wang

Keyword(s):

Time Series ◽

Short Term Memory ◽

Time Series Prediction ◽

Multivariate Time Series ◽

Short Term ◽

Term Memory ◽

Memory Network ◽

Long Short Term Memory

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2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Energies ◽

10.3390/en14092392 ◽

2021 ◽

Vol 14 (9) ◽

pp. 2392

Author(s):

Antonello Rosato ◽

Rodolfo Araneo ◽

Amedeo Andreotti ◽

Federico Succetti ◽

Massimo Panella

Keyword(s):

Time Series ◽

Short Term Memory ◽

Time Series Prediction ◽

Short Term ◽

Neural Architecture ◽

Network Layers ◽

Learning Scheme ◽

World Energy ◽

Multivariate Prediction ◽

Long Short Term Memory

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

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Using Multivariate Time Series Data via Long-Short Term Memory Network for Temperature Forecasting

Systems and Information Sciences - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-030-59194-6_4 ◽

2020 ◽

pp. 38-47

Author(s):

Jorge L. Charco ◽

Telmo Roque-Colt ◽

Kevin Egas-Arizala ◽

Charles M. Pérez-Espinoza ◽

Angélica Cruz-Chóez

Keyword(s):

Time Series ◽

Time Series Data ◽

Short Term Memory ◽

Multivariate Time Series ◽

Series Data ◽

Short Term ◽

Term Memory ◽

Temperature Forecasting ◽

Memory Network ◽

Long Short Term Memory

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Empirical Mode Decomposition, Extreme Learning Machine and Long Short-Term Memory for Time Series Prediction: A Comparative Study

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) ◽

10.1109/bracis.2018.00091 ◽

2018 ◽

Author(s):

Elivelto Ebermam ◽

Gabriel G. De Angelo ◽

Helder Knidel ◽

Renato A. Krohling

Keyword(s):

Time Series ◽

Comparative Study ◽

Extreme Learning Machine ◽

Short Term Memory ◽

Time Series Prediction ◽

Short Term ◽

Term Memory ◽

Mode Decomposition ◽

Long Short Term Memory ◽

Learning Machine

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Online reliability time series prediction via convolutional neural network and long short term memory for service-oriented systems

Knowledge-Based Systems ◽

10.1016/j.knosys.2018.07.006 ◽

2018 ◽

Vol 159 ◽

pp. 132-147 ◽

Cited By ~ 17

Author(s):

Hongbing Wang ◽

Zhengping Yang ◽

Qi Yu ◽

Tianjing Hong ◽

Xin Lin

Keyword(s):

Neural Network ◽

Time Series ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Time Series Prediction ◽

Short Term ◽

Term Memory ◽

Service Oriented ◽

Long Short Term Memory

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Deep Learning with Long Short-Term Memory for Time Series Prediction

IEEE Communications Magazine ◽

10.1109/mcom.2019.1800155 ◽

2019 ◽

Vol 57 (6) ◽

pp. 114-119 ◽

Cited By ~ 43

Author(s):

Yuxiu Hua ◽

Zhifeng Zhao ◽

Rongpeng Li ◽

Xianfu Chen ◽

Zhiming Liu ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Time Series Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Simplifying Long Short-Term Memory for Fast Training and Time Series Prediction

Journal of Physics Conference Series ◽

10.1088/1742-6596/1213/4/042039 ◽

2019 ◽

Vol 1213 ◽

pp. 042039 ◽

Cited By ~ 1

Author(s):

Yuyan Zhang ◽

Xin Hao ◽

Yong Liu

Keyword(s):

Time Series ◽

Short Term Memory ◽

Time Series Prediction ◽

Short Term ◽

Term Memory ◽

Fast Training ◽

Long Short Term Memory

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Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks

Scientific Reports ◽

10.1038/s41598-021-96751-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sujan Ghimire ◽

Zaher Mundher Yaseen ◽

Aitazaz A. Farooque ◽

Ravinesh C. Deo ◽

Ji Zhang ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Flood Control ◽

Performance Metrics ◽

Short Term Memory ◽

Short Term ◽

Neural Network Models ◽

Time Intervals ◽

Term Memory ◽

Long Short Term Memory

AbstractStreamflow (Qflow) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Qflow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Qflow time-series, while the LSTM networks use these features from CNN for Qflow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Qflow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Qflow, the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Qflow prediction error below the range of 0.05 m3 s−1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Qflow prediction.

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