python 时间序列预测——LSTM, GRU
数据集
太阳黑子数据集，Monthly Sunspots
下载
import numpy as np
import pandas as pd
url = "http://www.sidc.be/silso/INFO/snmtotcsv.php"
data = pd.read_csv (url,sep =";")
loc = "Monthly Sunspots.csv"
data . to_csv (loc , index = False )
data_csv = pd. read_csv (loc , header = None )
yt= data_csv . iloc [0:3210 ,3]
print(yt.head())
'''
0 96.7
1 104.3
2 116.7
3 92.8
4 141.7
Name: 3, dtype: float64
'''
print(yt.tail())
'''
3205 56.4
3206 54.1
3207 37.9
3208 51.5
3209 20.5
Name: 3, dtype: float64
'''
x_pacf=pacf(yt ,nlags=50, method='ols')
plt.plot(x_pacf)
该时间序列的 偏自相关函数【百度百科】
预处理
引入时滞
用紧邻的5个历史数据预测下一时刻
yt_1 =yt. shift (1)
yt_2 =yt. shift (2)
yt_3 =yt. shift (3)
yt_4 =yt. shift (4)
yt_5 =yt. shift (5)
data =pd. concat ([yt ,yt_1 , yt_2 ,yt_3 ,yt_4 ,yt_5 ], axis =1)
data . columns = ['yt', 'yt_1', 'yt_2', 'yt_3', 'yt_4', 'yt_5']
data = data . dropna () # 除去NULL，因为序列的起始点是没有历史的
print(data.tail( 6 ))
'''
yt yt_1 yt_2 yt_3 yt_4 yt_5
3204 57.0 58.0 62.2 63.6 78.6 64.4
3205 56.4 57.0 58.0 62.2 63.6 78.6
3206 54.1 56.4 57.0 58.0 62.2 63.6
3207 37.9 54.1 56.4 57.0 58.0 62.2
3208 51.5 37.9 54.1 56.4 57.0 58.0
3209 20.5 51.5 37.9 54.1 56.4 57.0
'''
print(data.head(6))
'''
yt yt_1 yt_2 yt_3 yt_4 yt_5
5 139.2 141.7 92.8 116.7 104.3 96.7
6 158.0 139.2 141.7 92.8 116.7 104.3
7 110.5 158.0 139.2 141.7 92.8 116.7
8 126.5 110.5 158.0 139.2 141.7 92.8
9 125.8 126.5 110.5 158.0 139.2 141.7
10 264.3 125.8 126.5 110.5 158.0 139.2
'''
y = data ['yt']
x = data ['yt_1', 'yt_2', 'yt_3', 'yt_4', 'yt_5']
归一化
scaler_x = preprocessing . MinMaxScaler (feature_range =(-1, 1))
x = np. array (x). reshape (( len(x) ,5 ))
x = scaler_x . fit_transform (x)
scaler_y = preprocessing . MinMaxScaler (
feature_range =( -1, 1))
y = np. array (y). reshape (( len(y), 1))
y = scaler_y . fit_transform (y)
train_end = 3042
x_train =x[0: train_end ,]
x_test =x[ train_end +1:3205 ,]
y_train =y[0: train_end ]
y_test =y[ train_end +1:3205]
x_train = x_train . reshape ( x_train . shape + (1 ,))
x_test = x_test . reshape ( x_test . shape + (1 ,))
print(x_train . shape) # (3042, 5, 1)
LSTM
from keras . layers . recurrent import LSTM
seed =2019
np.random.seed( seed )
model = Sequential()
model .add(LSTM (units =4, activation = 'tanh', recurrent_activation ='hard_sigmoid',input_shape = (5 , 1)))
model .add(Dense (units =1, activation = 'linear'))
model . compile ( loss ='mean_squared_error',optimizer = 'rmsprop')
model .fit( x_train , y_train , batch_size =1, epochs =10 , shuffle = True ) ## shuffle matters！！
print(model . summary ())
'''
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_16 (LSTM) (None, 4) 96
_________________________________________________________________
dense_64 (Dense) (None, 1) 5
=================================================================
Total params: 101
Trainable params: 101
Non-trainable params: 0
_________________________________________________________________
None
'''
score_train = model.evaluate (x_train , y_train , batch_size =1)
score_test = model.evaluate (x_test , y_test , batch_size =1)
print ("in train MSE = ", round( score_train,4))
print ("in test MSE = ", round( score_test ,4))
pred = model.predict(x_test)
# pred1 = scaler_y.inverse_transform(np.array(pred1).reshape((len(pred1), 1)))
plt.plot(y_test)
plt.plot(pred)
plt.legend(['target','prediction'])
训练时 shuffle
可以对比看看，不打乱数据集的训练效果会差一点。
打乱数据集：
不打乱数据集：
GRU
from keras . layers . recurrent import GRU
seed =2019
np. random . seed ( seed )
model = Sequential ()
model .add(GRU(units=4,
return_sequences =False ,
activation ='tanh',
recurrent_activation ='hard_sigmoid',
input_shape =(5 , 1)))
model .add(Dense(units =1, activation ='linear'))
model . compile (loss ='mean_squared_error',optimizer ='rmsprop')
print(model . summary ())
'''
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
gru_8 (GRU) (None, 4) 72
_________________________________________________________________
dense_23 (Dense) (None, 1) 5
=================================================================
Total params: 77
Trainable params: 77
Non-trainable params: 0
_________________________________________________________________
None
'''
model .fit( x_train , y_train , batch_size =1,epochs =10)
score_train = model . evaluate ( x_train ,y_train , batch_size =1)
score_test = model . evaluate (x_test , y_test , batch_size =1)
print ("in train MSE = ", round( score_train,5))
print ("in test MSE = ", round( score_test ,5))
pred1 = model . predict ( x_test )
# pred1 = scaler_y .inverse_transform (np. array(pred1).reshape((len(pred1), 1)))
plt.plot(y_test)
plt.plot(pred1)
plt.legend(['target','prediction'])
