python 时间序列预测——Jordan循环神经网络
数据集
COE 下载参见 python 时间序列预测——简单神经网络
设定输入输出变量并归一化
from sklearn import preprocessing
import pandas as pd
loc= "COE.csv "
temp = pd.read_csv ( loc )
data= temp . drop ( temp . columns [ [ 0 , 1 ] ] , axis=1)
x=data.drop ( data . columns [ [ 0 , 4 ] ] , axis =1)
x=x.apply(np.log )
x=pd.concat ( [ x , data ['Open?' ] ] , axis=1)
scaler_x = preprocessing . MinMaxScaler (feature_range=(0 , 1) )
x = np .array ( x ) . reshape ( ( len ( x ) ,4 ) )
x = scaler_x . fit_transform ( x )
y=data ['COE$']
scaler_y = preprocessing . MinMaxScaler (feature_range=(0 , 1) )
y = np . array ( y ) . reshape ( ( len ( y ) , 1) )
y = np . log ( y )
y = scaler_y . fit_transform ( y )
y = y.tolist ( )
x = x.tolist ( )
JordanRecurrent
import random
from pyneurgen.neuralnet import NeuralNet
from pyneurgen.recurrent import JordanRecurrent
random . seed (2019)
model = NeuralNet ()
input_nodes = 4
hidden_nodes = 7
output_nodes = 1
existing_weight_factor = 0.9
model.init_layers (input_nodes,
[hidden_nodes],
output_nodes,
JordanRecurrent(existing_weight_factor))
model. randomize_network ( )
model. layers[ 1 ] . set_activation_type('sigmoid')
model. set_learnrate(0.05)
model. set_all_inputs (x)
model. set_all_targets (y)
length = len ( x )
learn_end_point = int ( length * 0.95 )
model . set_learn_range ( 0 , learn_end_point )
model . set_test_range ( learn_end_point + 1 , length -1)
model . learn ( epochs =100 , show_epoch_results =True , random_testing = False )
mse = model. test ()
print(" test set MSE =",np.round(mse ,6))
target = np.array([ item [0][0] for item in fit1.test_targets_activations ])
pred = [ item [1][0] for item in fit1.test_targets_activations ]
# pred1 = scaler_y.inverse_transform (np.array( pred ). reshape((len(pred), 1))) # 恢复
# pred1 = np.exp(pred1)
plt.plot(target)
plt.plot(pred)
plt.plot(target*1.05,'--g',target*0.95,'--g')
plt.legend(['target','prediction','CI'])
