[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-...
Hybrid convolutional Bi-LSTM autoencoder framework for short-term wind speed predictionConvolutional neural network (CNN)Deep neural network modelsHybrid modelWind speed forecasting (WSF)Accurate wind speed prediction is essential for optimal operation and planning. The unstable and stochastic nature of ...
[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...
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,...
从某种意义上说,自动编码器试图只学习数据中最重要的特征,这里使用几个 LSTM 层(即LSTM Autoencoder)来捕获数据的时间依赖性。接下来我们一起看看如何将时间序列数据提供给自动编码器。 为了将序列分类为正常或异常,需要设定一个阈值,并规定高于该阈值时,心跳是异常的。
we leverage two existing deep generativeframeworks, namely variational Autoencoders (VAE) and Longitud...
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...
[10]CHEC, WANG H, FU Q, et al. Intelligent fault prediction of rolling bearing basedon gate recurrent unit and hybrid autoencoder[J/OL]. Proceedings of theInstitution of Mechanical Engineers, Part C: Journal of Mechanical...
The first difference concerns the objective of the prediction task of Schmidhuber’s model, which is predicting the next input from the previous inputs. In contrast, the LSTM-SAE model tries to reconstruct the inputs by establishing the LSTM autoencoder. Nevertheless, the major difference lies ...
作者构建了一组神经网络模型,统一称为“使用全面特征关系的LSTM结局预测”(long short-term memory(LSTM) outcome prediction using comprehensive feature relations, 简称为CLOUT)。通过该模型识别病人不同种类的离散临床特征的潜在空间,并且整合潜在空间表示到一个基于LSTM的预测模型框架。另外,设计了消融实验来识别CLOUT...