1.主要工作是将机械设备的传感器数据,LSTM-encoder-decoder模型输入正常数据时间序列训练模型,重构时间序列,然后使用异常数据进行测试,产生较高的重构错误,表明时间序列数据为异常的。 ps:在encoder-decoder模型中有score机制,较高的异常分数是更可能为异常的。
DyGCN-LSTM: A dynamic GCN-LSTM based encoder-decoder framework for multistep traffic predictionGraph neural networkLong-short term memorySpatio-temporal dataTemporal graphTime seriesTraffic predictionGRAPH CONVOLUTIONAL NETWORKNEURAL-NETWORKSFLOW PREDICTION...
A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction https://europepmc.org/article/pmc/pmc7252178
Shallow stacked S2SAE has one hidden layer after the input layer on the encoder side and one hidden layer before the output layer on the decoder side, whereas a deep stacked S2SAE has two hidden layers on both sides. All the above stacked S2SAE forecasting models were developed and ...
Lyu PY, Chen N, Mao SJ, Li M (2020) LSTM based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion. Process Saf Environ Prot 137:93–105. https://doi.org/10.1016/j.psep.2020.02.021 Article MATH Google Scholar Zhang YW, Guo HS, Lu ZH, Zhan L,...
This seq2seq structure has solved the time step issue because the steps of encoder input and decoder output can be different. Thus, the LSTM-based seq2seq structure was selected as the basic structure for this hourly rainfall-runoff modeling study. For the rainfall-runoff task, xi represents ...
Finally, our ANN architecture also includes an attention mechanism. Attention mechanisms applied to encoder-decoder architectures are placed as an intermediate layer between the encoder and the decoder. This layer allows the decoder pay more attention to specific parts of the fixed-size vector it ...
3, which consists of three main components: LSTM encoder, GAT encoder, and LSTM decoder. The model takes 3s vehicle trajectories as input, processes them through the GAT-LSTM model, and predicts 5s trajectories. Fig. 3 illustrates the operational framework of the system using six vehicles as ...
Zhang, J., Du, J., Dai, L.: A gru-based encoderdecoder approach with attention for online handwritten mathematical expression recognition. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 902–907. IEEE (2017) Zhang, X.-Y., Yin, F...
3, the LSTM-AE architecture consists of two LSTM layers; one runs as an encoder and the other runs as a decoder. The encoder layer takes the input sequence (\({x}_{1},{x}_{2},\,\mathrm{...,}\,{x}_{n}\)) and encodes it into a learned representation vector. Then, the ...