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 Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach

2021 ◽  
Vol 7 ◽  
pp. e682
Author(s):  
Mohamed Hassan Essai Ali ◽  
Ibrahim B.M. Taha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Channel State Information ◽  
Short Term Memory ◽  
Superior Performance ◽  
Short Term ◽  
Channel State ◽  
State Information ◽  
Term Memory ◽  
Long Short Term Memory

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.

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