import torch import torch.nn as nn import torch.optim as optim import numpy as np from sklearn.model_selection import train_test_split # 生成示例数据 np.ran
我们在PyTorch1.5.1版本中使用CUDA 10.2和PyTorch Geometric Library1.5.0实现了我们的方法及其变体,在带有Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz和4个NVIDIA RTX 2080Ti显卡的服务器上训练他们。模型使用学习率为 1\times10^{-3} 的Adam优化器进行训练, (\beta_1,\beta_2)=(0.9,0.99) 。我们训练...
PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series time-series pytorch forecasting autoencoder multivariate-timeseries attention-mechanisms lstm-autoencoder Updated Oct 4, 2023 Python chibui191 / bitcoin_volatility_forecasting Star 202 Code Issues Pull requests ...
多元时序聚类 https://paperswithcode.com/task/clustering-multivariate-time-series 另外,时序深度学习库tsai(https://timeseriesai.github.io/tsai/,github https://github.com/timeseriesAI/tsai)TST,ROCKET Pytorch,ROCKET,RNNAttention,RNNAttentionPlus,XCM,MINIROCKET,ConvTransPlus等模型支持multivariate time serie...
我们使用PyTorch-1.7.1库训练所有模型。 我们使用AdamW优化器来训练我们的模型,初始学习率为0.01(元学习率为0.02),步长为0.5的步长调度器。我们使用以下超参数值: 窗口大大小:10 transformer编码器的层数:1 编码器前馈单元的层数:2 编码器层中的隐藏单元:64 编码器中的dropout:0.1 第5节分析了窗口大小对异常检测...
All experiments are implemented in PyTorch [48] using a single NVIDIA GeForce RTX 3090. Table 2. The main features of datasets. DatasetTrainTestDimensionAnomalies (%) SWaT 496 800 449 919 51 11.98 SMD 708 405 708 420 38 4.16 MSL 58 317 73 729 55 10.72 PSM 132 481 87 841 25 27.75 ...
TIME SERIES IS WORTH 64 WORDS: LONG-TERM FORECASTING WITH TRANSFORMERS(ICLR 2023)中,提出了基于...
I would like to implement LSTM for multivariate input in Pytorch. Following this article https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/ which uses keras, the input data are in shape of (number of samples, number of timesteps, number of parallel fea...
PyTorch-Forecasting version: 1.0.0 PyTorch version: Latest Python version: 3.10 Operating System: macOS Ventura I have experience in time series forecasting using the Darts library. Now, I want to train a DeepAR model in combination with...
我们使用Pytorch版本1.7.0与CUDA 10.1和Pytorch几何库版本1.6.3实现我们的方法及其所有变体。所有实验均在4个NVIDIA Tesla P100 GPUs上进行。对于时间序列预测,我们将历史窗口大小设置为60,并将标签序列长度设置为30,以预测下一个时间戳的值。时间上下文建模的扩展卷积级别的数量被设置为3。此外,模型输入嵌入维数设置为...