data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据副本3.csv', index_col=0, parse_dates=True) data.index = pd.to_datetime(data.index, unit='s') data = data.dropna() # 绘制原始时间序列图 plt.plot(data) plt.title('Original Time Series') plt.show() # 对时间序列进行平稳化处理 predictions_diff = pd.Series(data.fittedvalues, copy=True) predictions_diff_cumsum = predictions_diff.cumsum() predictions = pd.Series(data['y'].iloc[0], index=data.index) predictions = predictions.add(predictions_diff_cumsum, fill_value=0) # 绘制平稳化后的时间序列图 plt.plot(predictions) plt.title('Stabilized Time Series') plt.show(),如何修改成功运行AttributeError: 'DataFrame' object has no attribute 'fittedvalues'
时间: 2024-02-09 22:09:36 浏览: 31
根据提示信息,这段代码出错的原因是 DataFrame 对象没有属性 'fittedvalues'。这可能是因为在对时间序列进行平稳化处理时,使用了某个模型对数据进行了拟合,但是没有将拟合后的结果保存在 DataFrame 对象中。因此,需要检查代码中是否存在这样的语句:
```python
model = SomeModel()
result = model.fit(data)
```
如果存在这样的语句,就需要将拟合后的结果保存在 DataFrame 对象中,例如:
```python
model = SomeModel()
result = model.fit(data)
data['fittedvalues'] = result.fittedvalues
```
然后再运行原来的平稳化代码即可。如果以上方法无法解决问题,你可以提供更多的代码和错误信息,以便更好地定位问题。
相关问题
data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据副本3.csv', index_col=0, parse_dates=True) data.index = pd.to_datetime(data.index, unit='s') data = data.dropna() # 绘制原始时间序列图 plt.plot(data) plt.title('Original Time Series') plt.show(),增加代码,输出平滑后的效果图
在对时间序列进行平稳化处理后,可以使用以下代码输出平滑后的效果图:
```python
# 对时间序列进行平稳化处理
predictions_diff = pd.Series(data['y'].values - data['y'].shift(1).values, index=data.index).dropna()
predictions_diff_cumsum = predictions_diff.cumsum()
predictions = pd.Series(data['y'].iloc[0], index=data.index)
predictions = predictions.add(predictions_diff_cumsum, fill_value=0)
# 绘制平滑后的时间序列图
plt.plot(predictions)
plt.title('Smoothed Time Series')
plt.show()
```
这段代码使用了差分的方法对时间序列进行平稳化处理,然后对平稳化后的时间序列进行绘图。注意,这里使用了 `data['y']` 来获取时间序列中的数值,因此需要确保数据集中存在名为 'y' 的列。
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM from sklearn.metrics import r2_score,median_absolute_error,mean_absolute_error # 读取数据 data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据rnn.csv') # 取出特征参数 X = data.iloc[:,2:].values # 数据归一化 scaler = MinMaxScaler(feature_range=(0, 1)) X[:, 0] = scaler.fit_transform(X[:, 0].reshape(-1, 1)).flatten() #X = scaler.fit_transform(X) #scaler.fit(X) #X = scaler.transform(X) # 划分训练集和测试集 train_size = int(len(X) * 0.8) test_size = len(X) - train_size train, test = X[0:train_size, :], X[train_size:len(X), :] # 转换为监督学习问题 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back - 1): a = dataset[i:(i + look_back), :] X.append(a) Y.append(dataset[i + look_back, 0]) return np.array(X), np.array(Y) look_back = 12 X_train, Y_train = create_dataset(train, look_back) #Y_train = train[:, 2:] # 取第三列及以后的数据 X_test, Y_test = create_dataset(test, look_back) #Y_test = test[:, 2:] # 取第三列及以后的数据 # 转换为3D张量 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 构建LSTM模型 model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1))) model.add(LSTM(units=50)) model.add(Dense(units=1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(X_train, Y_train, epochs=5, batch_size=32) #model.fit(X_train, Y_train.reshape(Y_train.shape[0], 1), epochs=10, batch_size=32) # 预测下一个月的销量 last_month_sales = data.tail(12).iloc[:,2:].values #last_month_sales = data.tail(1)[:,2:].values last_month_sales = scaler.transform(last_month_sales) last_month_sales = np.reshape(last_month_sales, (1, look_back, 1)) next_month_sales = model.predict(last_month_sales) next_month_sales = scaler.inverse_transform(next_month_sales) print('Next month sales: %.0f' % next_month_sales[0][0]) # 计算RMSE误差 rmse = np.sqrt(np.mean((next_month_sales - last_month_sales) ** 2)) print('Test RMSE: %.3f' % rmse)IndexError Traceback (most recent call last) Cell In[1], line 36 33 X_test, Y_test = create_dataset(test, look_back) 34 #Y_test = test[:, 2:] # 取第三列及以后的数据 35 # 转换为3D张量 ---> 36 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) 37 X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) 38 # 构建LSTM模型 IndexError: tuple index out of range代码修改
在代码中,X_train 和 X_test 的维度为 (样本数量,时间步长),需要将其转换为 (样本数量,时间步长,特征数量) 的形式。因此在创建数据集时,需要将数据 reshape 为 (样本数量,时间步长,1),即每个时间步长只有一个特征。代码如下:
```
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset) - look_back - 1):
a = dataset[i:(i + look_back), :]
X.append(a.reshape(-1, 1))
Y.append(dataset[i + look_back, 0])
return np.array(X), np.array(Y)
```
在训练和测试数据集转换为3D张量时,需要保证第三个维度为特征数量。因此需要修改如下代码:
```
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
```
修改后的完整代码如下:
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
from sklearn.metrics import r2_score,median_absolute_error,mean_absolute_error
# 读取数据
data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据rnn.csv')
# 取出特征参数
X = data.iloc[:,2:].values
# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X)
# 划分训练集和测试集
train_size = int(len(X) * 0.8)
test_size = len(X) - train_size
train, test = X[0:train_size, :], X[train_size:len(X), :]
# 转换为监督学习问题
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset) - look_back - 1):
a = dataset[i:(i + look_back), :]
X.append(a.reshape(-1, 1))
Y.append(dataset[i + look_back, 0])
return np.array(X), np.array(Y)
look_back = 12
X_train, Y_train = create_dataset(train, look_back)
X_test, Y_test = create_dataset(test, look_back)
# 转换为3D张量
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
# 构建LSTM模型
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(units=50))
model.add(Dense(units=1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, Y_train, epochs=5, batch_size=32)
# 预测下一个月的销量
last_month_sales = data.tail(12).iloc[:,2:].values
last_month_sales = scaler.transform(last_month_sales)
last_month_sales = np.reshape(last_month_sales, (1, look_back, 1))
next_month_sales = model.predict(last_month_sales)
next_month_sales = scaler.inverse_transform(next_month_sales)
print('Next month sales: %.0f' % next_month_sales[0][0])
# 计算RMSE误差
rmse = np.sqrt(np.mean((next_month_sales - last_month_sales) ** 2))
print('Test RMSE: %.3f' % rmse)
```