1#预测:2fut_pre = 1234test_inputs = train_data_normalized[-train_window:].tolist()5print(test_inputs)67model.eval()8foriinrange(fut_pre):9seq = torch.FloatTensor(test_inputs[-train_window:])10with torch.no_grad():11model.hidden = (torch.zeros(1,1,model.hidden_layer_size),12to...
根据过去一天的天气情况和污染状况以及下一个小时的「预期」天气条件,预测下一个小时的污染状况。 我们可以使用之前博客中编写的 series_to_supervised()函数来转换数据集: 如何用 Python 将时间序列问题转换为监督学习问题(https://machinelearningmastery.com/convert-time-series-supervised-learning-problem-python/) ...
3.2. Python实践代码 import pandas as pd import numpy as np import datetime from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.layers import LSTM,TimeDistributed,Dense,Dropout from tensorflow.keras.models import Sequential from tensorflow.keras.models import load_model ...
https://zh.gluon.ai/chapter_recurrent-neural-networks/lang-model.html 翻译自: https://stackabuse.com/seaborn-library-for-data-visualization-in-python-part-1/ https://stackabuse.com/time-series-prediction-using-lstm-with-pytorch-in-python/ ...
model=Sequential()# CNN层model.add(Conv1D(filters=64,kernel_size=2,activation='relu',input_shape=(X_train.shape[1],X_train.shape[2])))model.add(MaxPooling1D(pool_size=2))# LSTM层model.add(LSTM(50,return_sequences=True))model.add(Dropout(0.2))model.add(LSTM(50))model.add(Dropout(...
model_selection import train_test_split # 加载数据 data = pd.read_csv('multi_feature_time_series.csv') # 检查数据 print(data.head()) # 处理缺失值(如果有) data = data.dropna() # 归一化数据 scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(data) # 创建...
TFT(Transformer-based Time Series Forecasting)是一种基于Transformer模型的时序预测方法,该方法由谷歌DeepMind团队于2019年匠心独运地提出。其核心思想在于,通过巧妙地融入时间特征嵌入(Temporal Feature Embedding)和模态嵌入(Modality Embedding),使Transformer模型能够更精准地捕捉时序数据中的周期性与趋势性特征,并综合...
min_delta=0.001, patience=5 ): lstm_input = Input(shape=(n_lag, n_ft)) # Series signal lstm_layer = LSTM(n_layer, activation='relu')(lstm_input) x = Dense(n_outputs)(lstm_layer) self.model = Model(inputs=lstm_input, outputs=x) self.batch = batch ...
model = Sequential() model.add(LSTM(100, input_shape=(trainX.shape[1], trainX.shape[2]))) model.add(Dense(1)) model.compile(loss='mae', optimizer='adam') history =model.fit(trainX, trainY, epochs=300, batch_size=100, validation_data=(testX, testY), verbose=0, shuffle=False) ...
model.add(LSTM(200, return_sequences=True, input_shape=(X_train.shape[1],1))): 添加一个具有200个神经元的LSTM层到模型中。 input_shape=(X_train.shape[1], 1)指定了输入数据的形状,其中X_train.shape[1]表示时间步数。 model.add(Dropout(0.2)): 添加一个20%的Dropout层,这有助于防止过拟合...