This paper proposes a novel RUL prediction model named AA-LSTM. We use a Bi-LSTM-based autoencoder to extract degradation information contained in the time series data. Meanwhile, a generative adversarial network is used to assist the autoencoder in extracting abstract representation, and then a ...
[1] Deep and Confident Prediction for Time Series atUber: Lingxue Zhu, Nikolay Laptev [2] Time-series ExtremeEvent Forecasting withNeural Networks atUber: Nikolay Laptev, Jason Yosinski,Li Erran Li, Slawek Smyl via https://towardsdatascience.com/extreme-event-forecasting-with-lstm-autoencoders-...
[1] Deep and Confident Prediction for Time Series atUber: Lingxue Zhu, Nikolay Laptev [2] Time-series ExtremeEvent Forecasting with Neural Networks at Uber: Nikolay Laptev, Jason Yosinski,Li Erran Li, Slawek Smyl via https://towardsdatascience.com/extreme-event-forecasting-with-lstm-autoencoder...
super(RecurrentAutoencoder, self).__init__() self.encoder = Encoder(seq_len, n_features, embedding_dim).to(device) self.decoder = Decoder(seq_len, embedding_dim, n_features).to(device) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return 1. 2. 3. 4. 5. 6...
return prediction, (hidden, cell) ## LSTM Auto Encoder class LSTMAutoEncoder(nn.Module): ...
A deep learning framework for financial time series using stacked autoencoders and long short term memory The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet...
接下来将以GitHub[3]中的 LSTM Autoencoder为基础,并进行一些小调整。因为模型的工作是重建时间序列数据,因此该模型需要从编码器开始定义。 classEncoder(nn.Module):"""定义一个编码器的子类,继承父类 nn.Modul"""def__init__(self,seq_len,n_features,embedding_dim=64):super(Encoder,self).__init__()...
现场采集风冷冷水机组传感器数据，用于训练改进的LSTM。通过实验分析得出，不同传感器检测效率不同。将该文所提方法的检测结果与自动编码器（Autoencoder）、主元分析法（PCA）、标准的LSTM三种方法的检测结果进行比较，得出该文所提方法在冷水机组传感器偏差故障检测中检测效率明显优于其他三种方法；并且针对同一传感器相同...
Semi-supervised recursive autoencoders for predicting sentiment distributions. In Empirical Methods in Natural Language Processing (EMNLP), pp. 151–161. Edinburgh, 2011. Hochreiter, S., and Schmidhuber, J., Long short-term memory. Neural Comput. 9(8):1735–1780, 1997. Article CAS Google ...
For the autoencoder, the entropy function reduces into:(4)LHx,z=-∑k=1Nxklogzk+1-xklog1-zk Apart from using the traditional autoencoder, a special type of convolution branch is introduced for classification purpose. In this CNN-based block, in place of using conventional CNN, depthwise se...