cnn-lstm-ARIMA的股票代码，要求用过去五天预测未来八天的收盘价，输入特征有收盘价，最高价，最低价，换手率，涨跌额，涨跌幅，请详细解释每个步骤，写出代码，我要代码

步骤1：数据预处理

首先，需要加载数据，将数据转换为DataFrame格式，并将日期列设置为索引。然后，需要将特征数据进行标准化处理，以确保所有特征的值在相同的范围内。

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

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

# 标准化处理
scaler = MinMaxScaler()
data = scaler.fit_transform(data)

步骤2：划分训练集和测试集

将数据集划分为训练集和测试集。这里可以选择80:20或70:30的比例进行划分。

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

步骤3：构建模型

使用CNN-LSTM-ARIMA模型，首先需要定义CNN和LSTM模型。

from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv1D, MaxPooling1D, LSTM

# 定义CNN模型
def create_cnn_model():
    model = Sequential()
    model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(5, 6)))
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(Dropout(0.5))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(32, activation='relu'))
    return model

# 定义LSTM模型
def create_lstm_model():
    model = Sequential()
    model.add(LSTM(units=50, return_sequences=True, input_shape=(5, 32)))
    model.add(Dropout(0.2))
    model.add(LSTM(units=50, return_sequences=True))
    model.add(Dropout(0.2))
    model.add(LSTM(units=50))
    model.add(Dropout(0.2))
    return model

然后，将CNN和LSTM模型组合起来，并训练模型。

from statsmodels.tsa.arima_model import ARIMA

# 构建模型
def cnn_lstm_arima(train_data, test_data):
    # 构建CNN模型
    cnn_model = create_cnn_model()

    # 将数据转换成CNN模型的输入格式
    train_X = train_data.reshape((train_data.shape[0], 5, 6))
    test_X = test_data.reshape((test_data.shape[0], 5, 6))

    # 提取CNN模型的特征
    train_features = cnn_model.predict(train_X)
    test_features = cnn_model.predict(test_X)

    # 构建LSTM模型
    lstm_model = create_lstm_model()

    # 将CNN模型的特征作为LSTM模型的输入
    train_X = train_features.reshape((train_features.shape[0], 5, 32))
    test_X = test_features.reshape((test_features.shape[0], 5, 32))

    # 训练LSTM模型
    lstm_model.compile(loss='mean_squared_error', optimizer='adam')
    lstm_model.fit(train_X, train_y, epochs=50, batch_size=64, verbose=2)

    # 使用LSTM模型预测未来8天的收盘价
    lstm_predictions = lstm_model.predict(test_X)
    lstm_predictions = lstm_predictions.reshape((lstm_predictions.shape[0], 1))

    # 计算LSTM模型的均方误差
    lstm_rmse = np.sqrt(np.mean(np.square(test_y - lstm_predictions)))

    # 使用ARIMA模型预测未来8天的收盘价
    arima_model = ARIMA(train_data[:, 0], order=(2, 1, 2))
    arima_model_fit = arima_model.fit(disp=0)
    arima_predictions = arima_model_fit.forecast(steps=8)[0]

    # 计算ARIMA模型的均方误差
    arima_rmse = np.sqrt(np.mean(np.square(test_y - arima_predictions)))

    # 计算CNN-LSTM-ARIMA模型的预测值
    cnn_lstm_arima_predictions = (lstm_predictions + arima_predictions) / 2

    # 计算CNN-LSTM-ARIMA模型的均方误差
    cnn_lstm_arima_rmse = np.sqrt(np.mean(np.square(test_y - cnn_lstm_arima_predictions)))

    return cnn_lstm_arima_predictions, lstm_rmse, arima_rmse, cnn_lstm_arima_rmse

步骤4：评估模型

最后，使用均方误差评估模型的准确性。

# 评估模型
predictions, lstm_rmse, arima_rmse, cnn_lstm_arima_rmse = cnn_lstm_arima(train_data, test_data)
print('LSTM模型的均方误差：%.3f' % lstm_rmse)
print('ARIMA模型的均方误差：%.3f' % arima_rmse)
print('CNN-LSTM-ARIMA模型的均方误差：%.3f' % cnn_lstm_arima_rmse)

完整代码如下：