LSTM多变量时间序列代码
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dense, Dropout
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import seaborn as sns
df=pd.read_csv("train.csv",parse_dates=["Date"],index_col=[0])
df.shape
df.head()
df.tail()
test_split=round(len(df)*0.20)
test_split
df_for_training=df[:-1041]
df_for_testing=df[-1041:]
scaler = MinMaxScaler(feature_range=(0,1))
df_for_training_scaled = scaler.fit_transform(df_for_training)
#scaler = MinMaxScaler(feature_range=(0,1))
df_for_testing_scaled=scaler.transform(df_for_testing)
def createXY(dataset,n_past):
dataX = []
dataY = []
for i in range(n_past, len(dataset)):
dataX.append(dataset[i - n_past:i, 0:dataset.shape[1]])
dataY.append(dataset[i,0])
return np.array(dataX),np.array(dataY)
trainX,trainY=createXY(df_for_training_scaled,30)
testX,testY=createXY(df_for_testing_scaled,30)
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import GridSearchCV
def build_model(optimizer):
grid_model = Sequential()
grid_model.add(LSTM(50,return_sequences=True,input_shape=(30,5)))
grid_model.add(LSTM(50))
grid_model.add(Dropout(0.2))
grid_model.add(Dense(1))
grid_model.compile(loss = 'mse',optimizer = optimizer)
return grid_model
grid_model = KerasRegressor(build_fn=build_model,verbose=1,validation_data=(testX,testY))
parameters = {'batch_size' : [16,20],
'epochs' : [8,10],
'optimizer' : ['adam','Adadelta'] }
grid_search = GridSearchCV(estimator = grid_model,
param_grid = parameters,
cv = 2)
#grid_search = grid_search.fit(trainX,trainY)
grid_search = grid_search.fit(trainX,trainY)
#grid_search.best_params
my_model=grid_search.best_estimator_.model
prediction=my_model.predict(testX)
print("prediction\n", prediction)
print("\nPrediction Shape-",prediction.shape)
scaler.inverse_transform(prediction)
prediction_copies_array = np.repeat(prediction,5, axis=-1)
pred=scaler.inverse_transform(np.reshape(prediction_copies_array,(len(prediction),5)))[:,0]
original_copies_array = np.repeat(testY,5, axis=-1)
original_copies_array.shape
original=scaler.inverse_transform(np.reshape(original_copies_array,(len(testY),5)))[:,0]
import matplotlib.pyplot as plt
plt.plot(original, color = 'red', label = 'Real Stock Price')
plt.plot(pred, color = 'blue', label = 'Predicted Stock Price')
plt.title(' Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel(' Stock Price')
plt.legend()
plt.show()
df_30_days_past=df.iloc[-30:,:]
df_30_days_future=pd.read_csv("test.csv",parse_dates=["Date"],index_col=[0])
df_30_days_future["Open"]=0
df_30_days_future=df_30_days_future[["Open","High","Low","Close","Adj Close"]]
old_scaled_array=scaler.transform(df_30_days_past)
new_scaled_array=scaler.transform(df_30_days_future)
new_scaled_df=pd.DataFrame(new_scaled_array)
new_scaled_df.iloc[:,0]=np.nan
full_df=pd.concat([pd.DataFrame(old_scaled_array),new_scaled_df]).reset_index().drop(["index"],axis=1)
full_df_scaled_array=full_df.values
(60, 5)
all_data=[]
time_step=30
for i in range(time_step,len(full_df_scaled_array)):
data_x=[]
data_x.append(full_df_scaled_array[i-time_step:i,0:full_df_scaled_array.shape[1]])
data_x=np.array(data_x)
prediction=my_model.predict(data_x)
all_data.append(prediction)
full_df.iloc[i,0]=prediction
new_array=np.array(all_data)
new_array=new_array.reshape(-1,1)
prediction_copies_array = np.repeat(new_array,5, axis=-1)
y_pred_future_30_days = scaler.inverse_transform(np.reshape(prediction_copies_array,(len(new_array),5)))[:,0]
from tensorflow.keras.models import Model
from tensorflow.keras.models import load_model
my_model.save('Model_future_value.h5')
print('Model Saved!')
scaler
import pickle
scalerfile = 'scaler_model_future_value.pkl'
pickle.dump(scaler, open(scalerfile, 'wb'))
错误提示LFile "G:/时间序列/Multivariate-time-series-forecasting-using-LSTM-main/forecast.py", line 57, in
grid_search = grid_search.fit(trainX,trainY)
File "C:\Users\dell\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\model_selection_search.py", line 805, in fit
base_estimator = clone(self.estimator)
File "C:\Users\dell\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\base.py", line 92, in clone
"either does not set or modifies parameter %s" % (estimator, name)
RuntimeError: Cannot clone object <tensorflow.python.keras.wrappers.scikit_learn.KerasRegressor object at 0x0000017C9E8D5828>, as the constructor either does not set or modifies parameter validation_data
