本文展示了使用时序卷积网络（TCN）进行时间序列预测的全过程，包含详细的注释。整个过程主要包括：数据导入、数据清洗、结构转化、建立TCN模型、训练模型（包括动态调整学习率和earlystopping的设置）、预测、结果展示、误差评估等完整的时间序列预测流程。
本文使用的tcn库在本人上传的资源中，链接为tcn.py
本文使用的数据集在本人上传的资源中，链接为mock_kaggle.csv
import pandas as pd
import numpy as np
import math
from matplotlib import pyplot as plt
from matplotlib.pylab import mpl
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler
from keras import backend as K
from keras.layers import LeakyReLU
from tcn import TCN,tcn_full_summary
from sklearn.metrics import mean_squared_error # 均方误差
from keras.callbacks import LearningRateScheduler
from keras.callbacks import EarlyStopping
from tensorflow.keras import Input, Model,Sequentialmpl.rcParams['font.sans-serif'] = ['SimHei'] #显示中文
mpl.rcParams['axes.unicode_minus']=False #显示负号
取数据
data=pd.read_csv('mock_kaggle.csv',encoding ='gbk',parse_dates=['datetime'])
Date=pd.to_datetime(data.datetime)
data['date'] = Date.map(lambda x: x.strftime('%Y-%m-%d'))
datanew=data.set_index(Date)
series = pd.Series(datanew['股票'].values, index=datanew['date'])
series
date
2014-01-01 4972
2014-01-02 4902
2014-01-03 4843
2014-01-04 4750
2014-01-05 4654...
2016-07-27 3179
2016-07-28 3071
2016-07-29 4095
2016-07-30 3825
2016-07-31 3642
Length: 937, dtype: int64
滞后扩充数据
dataframe1 = pd.DataFrame()
num_hour = 16
for i in range(num_hour,0,-1):dataframe1['t-'+str(i)] = series.shift(i)
dataframe1['t'] = series.values
dataframe3=dataframe1.dropna()
dataframe3.index=range(len(dataframe3))
dataframe3
| t-16 | t-15 | t-14 | t-13 | t-12 | t-11 | t-10 | t-9 | t-8 | t-7 | t-6 | t-5 | t-4 | t-3 | t-2 | t-1 | t | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4972.0 | 4902.0 | 4843.0 | 4750.0 | 4654.0 | 4509.0 | 4329.0 | 4104.0 | 4459.0 | 5043.0 | 5239.0 | 5118.0 | 4984.0 | 4904.0 | 4822.0 | 4728.0 | 4464 |
| 1 | 4902.0 | 4843.0 | 4750.0 | 4654.0 | 4509.0 | 4329.0 | 4104.0 | 4459.0 | 5043.0 | 5239.0 | 5118.0 | 4984.0 | 4904.0 | 4822.0 | 4728.0 | 4464.0 | 4265 |
| 2 | 4843.0 | 4750.0 | 4654.0 | 4509.0 | 4329.0 | 4104.0 | 4459.0 | 5043.0 | 5239.0 | 5118.0 | 4984.0 | 4904.0 | 4822.0 | 4728.0 | 4464.0 | 4265.0 | 4161 |
| 3 | 4750.0 | 4654.0 | 4509.0 | 4329.0 | 4104.0 | 4459.0 | 5043.0 | 5239.0 | 5118.0 | 4984.0 | 4904.0 | 4822.0 | 4728.0 | 4464.0 | 4265.0 | 4161.0 | 4091 |
| 4 | 4654.0 | 4509.0 | 4329.0 | 4104.0 | 4459.0 | 5043.0 | 5239.0 | 5118.0 | 4984.0 | 4904.0 | 4822.0 | 4728.0 | 4464.0 | 4265.0 | 4161.0 | 4091.0 | 3964 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 916 | 1939.0 | 1967.0 | 1670.0 | 1532.0 | 1343.0 | 1022.0 | 813.0 | 1420.0 | 1359.0 | 1075.0 | 1015.0 | 917.0 | 1550.0 | 1420.0 | 1358.0 | 2893.0 | 3179 |
| 917 | 1967.0 | 1670.0 | 1532.0 | 1343.0 | 1022.0 | 813.0 | 1420.0 | 1359.0 | 1075.0 | 1015.0 | 917.0 | 1550.0 | 1420.0 | 1358.0 | 2893.0 | 3179.0 | 3071 |
| 918 | 1670.0 | 1532.0 | 1343.0 | 1022.0 | 813.0 | 1420.0 | 1359.0 | 1075.0 | 1015.0 | 917.0 | 1550.0 | 1420.0 | 1358.0 | 2893.0 | 3179.0 | 3071.0 | 4095 |
| 919 | 1532.0 | 1343.0 | 1022.0 | 813.0 | 1420.0 | 1359.0 | 1075.0 | 1015.0 | 917.0 | 1550.0 | 1420.0 | 1358.0 | 2893.0 | 3179.0 | 3071.0 | 4095.0 | 3825 |
| 920 | 1343.0 | 1022.0 | 813.0 | 1420.0 | 1359.0 | 1075.0 | 1015.0 | 917.0 | 1550.0 | 1420.0 | 1358.0 | 2893.0 | 3179.0 | 3071.0 | 4095.0 | 3825.0 | 3642 |
921 rows × 17 columns
二折划分数据并标准化
# pot=int(len(dataframe3)*0.8)
pd.DataFrame(np.random.shuffle(dataframe3.values)) #shuffle
pot=len(dataframe3)-12
train=dataframe3[:pot]
test=dataframe3[pot:]
scaler = MinMaxScaler(feature_range=(0, 1)).fit(train)
#scaler = preprocessing.StandardScaler().fit(train)
train_norm=pd.DataFrame(scaler.fit_transform(train))
test_norm=pd.DataFrame(scaler.transform(test))
test_norm.shape,train_norm.shape
((12, 17), (909, 17))
X_train=train_norm.iloc[:,:-1]
X_test=test_norm.iloc[:,:-1]
Y_train=train_norm.iloc[:,-1:]
Y_test=test_norm.iloc[:,-1:]
转换为3维数据 [samples, timesteps, features]
source_x_train=X_train
source_x_test=X_test
X_train=X_train.values.reshape([X_train.shape[0],1,X_train.shape[1]]) #从(909,16)-->(909,1,16)
X_test=X_test.values.reshape([X_test.shape[0],1,X_test.shape[1]]) #从(12,16)-->(12,1,16)
Y_train=Y_train.values
Y_test=Y_test.values
X_train.shape,Y_train.shape
((909, 1, 16), (909, 1))
X_test.shape,Y_test.shape
((12, 1, 16), (12, 1))
type(X_train),type(Y_test)
(numpy.ndarray, numpy.ndarray)
动态调整学习率与提前终止函数
def scheduler(epoch):# 每隔50个epoch，学习率减小为原来的1/10if epoch % 50 == 0 and epoch != 0:lr = K.get_value(tcn.optimizer.lr)if lr>1e-5:K.set_value(tcn.optimizer.lr, lr * 0.1)print("lr changed to {}".format(lr * 0.1))return K.get_value(tcn.optimizer.lr)reduce_lr = LearningRateScheduler(scheduler)
early_stopping = EarlyStopping(monitor='loss', patience=20, min_delta=1e-5,mode='auto',restore_best_weights=False,#是否从具有监测数量的最佳值的时期恢复模型权重verbose=2)
构造TCN模型
batch_size=None
timesteps=X_train.shape[1]
input_dim=X_train.shape[2] #输入维数
tcn = Sequential()
input_layer =Input(batch_shape=(batch_size,timesteps,input_dim))
tcn.add(input_layer)
tcn.add(TCN(nb_filters=64, #在卷积层中使用的过滤器数。可以是列表。kernel_size=3, #在每个卷积层中使用的内核大小。nb_stacks=1, #要使用的残差块的堆栈数。dilations=[2 ** i for i in range(6)], #扩张列表。示例为：[1、2、4、8、16、32、64]。#用于卷积层中的填充,值为'causal' 或'same'。#“causal”将产生因果（膨胀的）卷积，即output[t]不依赖于input[t+1：]。当对不能违反时间顺序的时序信号建模时有用。#“same”代表保留边界处的卷积结果，通常会导致输出shape与输入shape相同。padding='causal',use_skip_connections=True, #是否要添加从输入到每个残差块的跳过连接。dropout_rate=0.1, #在0到1之间浮动。要下降的输入单位的分数。return_sequences=False,#是返回输出序列中的最后一个输出还是完整序列。activation='relu', #残差块中使用的激活函数 o = Activation(x + F(x)).kernel_initializer='he_normal', #内核权重矩阵（Conv1D）的初始化程序。use_batch_norm=True, #是否在残差层中使用批处理规范化。use_layer_norm=True, #是否在残差层中使用层归一化。name='tcn' #使用多个TCN时，要使用唯一的名称))
tcn.add(tf.keras.layers.Dense(64))
tcn.add(tf.keras.layers.LeakyReLU(alpha=0.3))
tcn.add(tf.keras.layers.Dense(32))
tcn.add(tf.keras.layers.LeakyReLU(alpha=0.3))
tcn.add(tf.keras.layers.Dense(16))
tcn.add(tf.keras.layers.LeakyReLU(alpha=0.3))
tcn.add(tf.keras.layers.Dense(1))
tcn.add(tf.keras.layers.LeakyReLU(alpha=0.3))
tcn.compile('adam', loss='mse', metrics=['accuracy'])
tcn.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
tcn (TCN) (None, 64) 143168
_________________________________________________________________
dense (Dense) (None, 64) 4160
_________________________________________________________________
leaky_re_lu (LeakyReLU) (None, 64) 0
_________________________________________________________________
dense_1 (Dense) (None, 32) 2080
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 32) 0
_________________________________________________________________
dense_2 (Dense) (None, 16) 528
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, 16) 0
_________________________________________________________________
dense_3 (Dense) (None, 1) 17
_________________________________________________________________
leaky_re_lu_3 (LeakyReLU) (None, 1) 0
=================================================================
Total params: 149,953
Trainable params: 148,417
Non-trainable params: 1,536
_________________________________________________________________
训练
history=tcn.fit(X_train,Y_train, epochs=80,batch_size=32,callbacks=[reduce_lr])
Train on 909 samples
Epoch 1/80
909/909 [==============================] - 14s 16ms/sample - loss: 0.1332 - accuracy: 0.0187......
Epoch 10/80
909/909 [==============================] - 1s 1ms/sample - loss: 0.0135 - accuracy: 0.0187......
Epoch 20/80
909/909 [==============================] - 1s 1ms/sample - loss: 0.0091 - accuracy: 0.0187......
Epoch 30/80
909/909 [==============================] - 1s 1ms/sample - loss: 0.0090 - accuracy: 0.0176......
Epoch 40/80
909/909 [==============================] - 1s 1ms/sample - loss: 0.0059 - accuracy: 0.0187......
Epoch 50/80
909/909 [==============================] - 1s 1ms/sample - loss: 0.0067 - accuracy: 0.0187
lr changed to 0.00010000000474974513......
Epoch 60/80
909/909 [==============================] - 1s 1ms/sample - loss: 0.0047 - accuracy: 0.0187......
Epoch 70/80
909/909 [==============================] - 1s 1ms/sample - loss:
......
Epoch 80/80
909/909 [==============================] - 1s 1ms/sample - loss: 0.0050 - accuracy: 0.0187
history.history.keys() #查看history中存储了哪些参数
plt.plot(history.epoch,history.history.get('loss')) #画出随着epoch增大loss的变化图
#plt.plot(history.epoch,history.history.get('acc'))#画出随着epoch增大准确率的变化图
预测
predict = tcn.predict(X_test)
real_predict=scaler.inverse_transform(np.concatenate((source_x_test,predict),axis=1))
real_y=scaler.inverse_transform(np.concatenate((source_x_test,Y_test),axis=1))
real_predict=real_predict[:,-1]
real_y=real_y[:,-1]
误差评估
plt.figure(figsize=(15,6))
bwith = 0.75 #边框宽度设置为2
ax = plt.gca()#获取边框
ax.spines['bottom'].set_linewidth(bwith)
ax.spines['left'].set_linewidth(bwith)
ax.spines['top'].set_linewidth(bwith)
ax.spines['right'].set_linewidth(bwith)
plt.plot(real_predict,label='real_predict')
plt.plot(real_y,label='real_y')
plt.plot(real_y*(1+0.15),label='15%上限',linestyle='--',color='green')
# plt.plot(real_y*(1+0.1),label='10%上限',linestyle='--')
# plt.plot(real_y*(1-0.1),label='10%下限',linestyle='--')
plt.plot(real_y*(1-0.15),label='15%下限',linestyle='--',color='green')
plt.fill_between(range(0,12),real_y*(1+0.15),real_y*(1-0.15),color='gray',alpha=0.2)
plt.legend()
plt.show()
round(mean_squared_error(Y_test,predict),4)
0.0012
from sklearn.metrics import r2_score
round(r2_score(real_y,real_predict),4)
0.5192
per_real_loss=(real_y-real_predict)/real_y
avg_per_real_loss=sum(abs(per_real_loss))/len(per_real_loss)
print(avg_per_real_loss)
0.13984217335814073
#计算指定置信水平下的预测准确率
#level为小数
def comput_acc(real,predict,level):num_error=0for i in range(len(real)):if abs(real[i]-predict[i])/real[i]>level:num_error+=1return 1-num_error/len(real)
comput_acc(real_y,real_predict,0.2),comput_acc(real_y,real_predict,0.15),comput_acc(real_y,real_predict,0.1)
(0.75, 0.6666666666666667, 0.33333333333333337)
