根据上述数据建立，lARIMA-RNN模型，帮我写出python代码

在Python中，我们可以使用`statsmodels`, `pandas`, 和 `keras` 这些库来构建混合模型，如ARIMA (自回归整合滑动平均) 和 RNN (循环神经网络)。下面是一个简单的示例，展示如何结合这两个模型：

import numpy as np
import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense

# 假设我们有一个名为df的数据框，其中包含时间序列数据
data = pd.read_csv('your_data.csv') # 替换为你的数据文件路径

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data['target_column']) # 'target_column' 是你要预测的列名

# 分割训练集和测试集
train_size = int(len(scaled_data) * 0.7)
test_size = len(scaled_data) - train_size
train, test = scaled_data[0:train_size,:], scaled_data[train_size:len(scaled_data),:]

# 转换为适合RNN的时间序列数据
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 = 1  # 指定ARIMA的滞后步数
X_train, y_train = create_dataset(train, look_back)
X_test, y_test = create_dataset(test, look_back)

# 构建ARIMA模型
model_arima = ARIMA(order=(5,1,0))  # 这里的参数根据实际情况调整
arima_model = model_arima.fit(X_train)

# 使用ARIMA的结果初始化RNN的输入
initial_conditions = arima_model.predict(start=len(X_train), end=len(X_train)+len(X_test)-1, dynamic=False)

# 建立LSTM模型
rnn_model = Sequential()
rnn_model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
rnn_model.add(LSTM(units=50))
rnn_model.add(Dense(1))

# 编译并拟合模型
rnn_model.compile(optimizer='adam', loss='mean_squared_error')
rnn_model.fit(X_train, y_train[:, np.newaxis], epochs=100, batch_size=1, initial_state=initial_conditions)

# 结合ARIMA和RNN的预测
final_predictions = []
arima_pred = arima_model.predict(X_test)
for i in range(len(arima_pred)):
    final_predictions.append(arima_pred[i][0])
    final_predictions.append(rnn_model.predict(np.array([X_test[i]]))[0])

# 合并结果并反归一化
final_predictions = scaler.inverse_transform(final_predictions.reshape(-1,1))

# 打印预测值
print("Final predictions:", final_predictions)