TIME SERIES PREDICTION OF WAVE HEIGHT BY LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK

2021 ◽  
Vol 77 (2) ◽  
pp. I_151-I_156
Author(s):  
Nagisa SUMITANI ◽  
Tomohiro YASUDA ◽  
Nobuhito MORI ◽  
Tomoya SHIMURA
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wave Height ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Prediction of Rice Yield via Stacked LSTM

2020 ◽  
Vol 11 (1) ◽  
pp. 86-95
Author(s):  
Xiangyan Meng ◽  
Muyan Liu ◽  
Qiufeng Wu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Short Term Memory ◽  
Rice Yield ◽  
Time Series Prediction ◽  
Superior Performance ◽  
Heilongjiang Province ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In order to guarantee the rice yield more effectively, the prediction of rice yield should be taken into account. Because the rice yield every year can be seen as a sequence of time series, many methods applied in prediction of time series can be considered. Long Short-Term Memory recurrent neural network (LSTM) is one of the most popular methods of time series prediction. In consideration of its own characteristics and the popularity of deep learning, an improved LSTM architecture called Stacked LSTM which has multiple layers is proposed in this article. It is based on the idea of increasing the depth of LSTM. The comparison among the Stacked LSTM architectures which have different numbers of LSTM layers and other methods including ARIMA, GRU, and ANN has been carried out on the data of rice yield in Heilongjiang Province, China, from 1980 to 2017. The results showed the superior performance of Stacked LSTM and the effectiveness of increasing the depth of LSTM.

Prediction of InSAR deformation time-series using a long short-term memory neural network

2021 ◽  
Vol 42 (18) ◽  
pp. 6921-6944
Author(s):  
Yi Chen ◽  
Yi He ◽  
Lifeng Zhang ◽  
Youdong Chen ◽  
Hongyu Pu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

2018 ◽  
Vol 7 (4.15) ◽  
pp. 25 ◽  
Author(s):  
Said Jadid Abdulkadir ◽  
Hitham Alhussian ◽  
Muhammad Nazmi ◽  
Asim A Elsheikh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.  

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

2019 ◽  
Vol 57 (6) ◽  
pp. 114-119 ◽  
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

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

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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