根据上述数据建立,lARIMA-RNN模型,帮我写出python代码
时间: 2024-09-24 12:04:47 浏览: 35
python课程设计-基于RNN+LSTM写诗文python源码+文档说明
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:
```python
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)
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