使用keras里面的lstm进行时间序列预测_实验：使用LSTM递归神经网络进行时间序列预测...

好的，让我们开始使用Keras中的LSTM模型进行时间序列预测。

首先，让我们导入所需的库：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from keras.models import Sequential
from keras.layers import Dense, LSTM
from sklearn.preprocessing import MinMaxScaler

接下来，我们将加载数据并进行预处理：

# 加载数据
data = pd.read_csv('data.csv', index_col=0)

# 将数据转换为numpy数组
dataset = data.values

# 将数据类型转换为浮点型
dataset = dataset.astype('float32')

# 将数据缩放至0到1的范围内
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# 将数据拆分为训练数据和测试数据
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train_data, test_data = dataset[0:train_size, :], dataset[train_size:len(dataset), :]

# 将数据转换为适合LSTM输入的格式
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset) - look_back - 1):
        a = dataset[i:(i + look_back), 0]
        X.append(a)
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

look_back = 5
train_X, train_Y = create_dataset(train_data, look_back)
test_X, test_Y = create_dataset(test_data, look_back)

# 将输入数据重塑为LSTM输入的3D格式
train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))
test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))

现在，我们将创建LSTM模型并进行训练：

# 创建LSTM模型
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(train_X, train_Y, epochs=100, batch_size=1, verbose=2)

# 使用训练好的模型进行预测
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)

# 将预测结果反缩放回原始范围
train_predict = scaler.inverse_transform(train_predict)
train_Y = scaler.inverse_transform([train_Y])
test_predict = scaler.inverse_transform(test_predict)
test_Y = scaler.inverse_transform([test_Y])

# 计算均方根误差
train_score = np.sqrt(mean_squared_error(train_Y[0], train_predict[:, 0]))
test_score = np.sqrt(mean_squared_error(test_Y[0], test_predict[:, 0]))
print('Train Score: %.2f RMSE' % (train_score))
print('Test Score: %.2f RMSE' % (test_score))

最后，我们将绘制训练和测试数据的预测结果以及实际值：

# 绘制训练数据的预测结果和实际值
train_predict_plot = np.empty_like(dataset)
train_predict_plot[:, :] = np.nan
train_predict_plot[look_back:len(train_predict) + look_back, :] = train_predict

plt.plot(scaler.inverse_transform(dataset))
plt.plot(train_predict_plot)
plt.show()

# 绘制测试数据的预测结果和实际值
test_predict_plot = np.empty_like(dataset)
test_predict_plot[:, :] = np.nan
test_predict_plot[len(train_predict) + (look_back * 2) + 1:len(dataset) - 1, :] = test_predict

plt.plot(scaler.inverse_transform(dataset))
plt.plot(test_predict_plot)
plt.show()

这就是使用Keras中的LSTM模型进行时间序列预测的基本流程。