Long short-term memory (LSTM) is regarded as one of the most popular methods for regression prediction of time series. In the memory unit of LSTM, since most values of gate structures are usually in the middle state (around 0.5), gate structures cannot effectively retain important information ...
lstm_layers=1, num_attention_heads=4, dropout=0.1, batch_size=16, n_epochs=10, add_relative_index=False, add_encoders=None, likelihood=QuantileRegression( quantiles=quantiles ), # QuantileRegression is set per default # loss_fn=MSELoss(), random_state=42, ) 8. 模型训练 my_model.fit(...
the LSTM model has a “forget gate” structure for adjusting the period to maintain the information. Since the forget gate widens the usage of past information, LSTM can handle long time-series data well. This property will be helpful
An attention enhanced graph convolutional LSTM network for skeleton-based action recognition. (2019) CoRR abs/1902.09130, URL http://dblp.uni-trier.de/db/journals/corr/corr1902.html#abs-1902-09130 Google Scholar [32] K. Cheng, Y. Zhang, X. He, W. Chen, J. Cheng, H. Lu, Skeleton-bas...
[14] developed a long-term accurate trajectory prediction model by improving Bi-LSTM model. Based on the predicted trajectory, a data transmission algorithm based on the predicted results is proposed to achieve efficient communication performance. Combining the confidence of each prediction step, [15]...
County-level soybean yield prediction using deep CNN-LSTM model[J]. Sensors,2019,19(20):4363. DOI: 10.3390/s19204363. [13] FERNANDEZ-BELTRAN R, BAIDAR T, KANG J, et al. Rice-yield prediction with multi-temporal sentinel-2 data and 3D CNN: A case study in Nepal[J]. Remote Sensing...
It trains a long short-term memory (LSTM) model and a gated recurrent unit (GRU) model. Both are recurrent neural networks (RNNs) that have been revealed to be powerful in time series forecasting. Specifically, LSTM model is able to learn long term...
Firstly, training samples are input in pairs to the feature extraction network, and a combination of one-dimensional convolution neural network (1DCNN) and long short-term memory (LSTM) network is introduced to extract features of the time-series data, thus enhancing the effectiveness and ...
time series prediction task and employ a data-driven approach using two state-of-the-art machine learning concepts, namely deep neural networks (DNNs) and long short-term memory (LSTM) recurrent networks, to minimise the amounts of over/under reservations within a short-time prediction context....
Thirdly, the forecasting performance of these two proposed hybrid models on the S2 manifold, namely Lie-LSTMOLS and Lie-LSTMNLS, are compared with those of the reference LieOLS and LieNLS models. The in-sample and out-of-sample results show that our proposed methods can achieve improved ...