[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-...
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 ...
如下图所示,我们训练LSTM Autoencoder作为我们模型的第一部分:自动特征提取,这对于大量捕获复杂的动态时间序列是很重要的。特征向量通过拼接后作为新的输入传到LSTM Forecaster模块中做预测(autoencoder模块输入的是多个时间序列,这里是拼接好的单一向量)。 我们的forecaster模块的工作流程十分好理解:我们有一个初始的窗口,...
[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...
内容提示: A NOVEL AUTOENCODERS-LSTM MODEL FOR STROKE OUTCOME PREDICTIONUSING MULTIMODAL MRI DATANima Hatami 1 Laura Mechtouff 2,3 David Rousseau 4 Tae-Hee Cho 2,3 Carole Frindel 11 CREATIS, CNRS UMR5220, INSERM U1206, Université Lyon 1, INSA-Lyon, France2 Stroke Department, Hospices ...
我们首先使用这些数据去训练我们LSTM Autoencoder, 之后删除encoder模块,并利用它制作特征生成器,最后再去训练我们forecaster模块中的LSTM 模型做预测任务。基于真实/存在的回归值和人造向量我们可以获得的下周的鳄梨预测价格。 图片来自于Time-series ExtremeEvent Forecasting with Neural Networks at Uber...
Finally, we can create a composite LSTM Autoencoder that has a single encoder and two decoders, one for reconstruction and one for prediction. We can implement this multi-output model in Keras using the functional API. You can learn more about the functional API in this post: How to Use ...
x = self.encoder(x) x = self.decoder(x) return 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 自动编码器类已经定义好,接下来创建一个它的实例。 model = RecurrentAutoencoder(seq_len, n_features, 128) model = model.to(device) ...
LSTM Autoencoder for Multivariate Data Figure 1. An LSTM Autoencoder. In our problem, we have a multivariate time-series data. A multivariate time-series data contains multiple variables observed over a period of time. We will build an LSTM autoencoder on this multivariate time-series to perfor...
[2]宋亚,夏唐斌,郑宇等.基于Autoencoder-BLSTM的涡扇发动机剩余寿命预测[J].计算机集成制造系统,2019,25(07):1611-1619. [3][1]Cheng P,Yufeng C,Qing C, et al. A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion[J]. Sensors,2021,21(2). ...