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
nhid = 50 # Number of nodes in the hidden layern_dnn_layers = 5 # Number of hidden fully connected layersnout = 1 # Prediction Windowsequence_len = 180 # Training Window# Number of features (since this is a univariate timeseries we'll set# this to 1 -- multivariate analysis is comi...
# Number of features (since this is a univariate timeseries we'll set# this to 1 -- multivariate analysis is coming in the future)ninp = 1 # Device selection (CPU | GPU)USE_CUDA = torch.cuda.is_available()device = 'cuda' i...
# Number of features (since this is a univariate timeseries we'll set # this to 1 -- multivariate analysis is coming in the future) ninp=1 # Device selection (CPU | GPU) USE_CUDA=torch.cuda.is_available() device='cuda'ifUSE_CUDAelse'cpu' # Initialize the model model=LSTMForecaster(...
importpandasaspd# 读取数据data=pd.read_csv('multivariate_time_series.csv')# 将CSV文件路径替换为实际路径print(data.head())# 打印前五行 1. 2. 3. 4. 5. 2. 数据预处理 数据预处理是一个关键步骤,通常包括归一化和划分训练集与测试集。
通过上述步骤,你可以使用PyTorch实现多变量时间序列预测。当然,这只是一个基本的示例,你可以根据自己的需求对模型进行改进和优化。例如,你可以尝试使用不同的模型架构(如CNN-LSTM混合模型、Transformer模型等),或者调整模型的超参数以获得更好的性能。
2.Multivariate input LSTM in pytorchHow to Develop LSTM Models for Time Series Forecasting 下面这篇介绍了好几个应用LSTM的案例,但是是用Keras写的,我的项目中主要参考了Multiple Parallel Series的相关思想 上面这篇是Multivariate input LSTM 这个案例的pytorch写法 转载一下Multiple Parallel Series的内容吧(机翻...
# first dense after lstm self.fc1 = nn.Linear(n_hidden * sequence_len, n_hidden) # Dropout layer self.dropout = nn.Dropout(p=dropout) # Create fully connected layers (n_hidden x n_deep_layers) dnn_layers = [] for i in range(n_deep_layers): ...
# this to 1 -- multivariate analysis is coming in the future) ninp = 1 # Device selection (CPU | GPU) USE_CUDA = torch.cuda.is_available() device = 'cuda' if USE_CUDA else 'cpu' # Initialize the model model = LSTMForecaster(ninp, nhid, nout, sequence_len, n_deep_layers=n_...
# this to 1 -- multivariate analysis is coming in the future) ninp =1 # Device selection (CPU | GPU) USE_CUDA = torch.cuda.is_available device ='cuda'ifUSE_CUDAelse'cpu' # Initialize the model model = LSTMForecaster(ninp, nhid, nout, sequence_len, n_deep_layers=n_dnn_layers, ...