单变量时间序列预测
数据类型：单列
import numpy
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential, load_model
dataset = df.values
# 将整型变为float
dataset = dataset.astype('float32')
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
train_size = int(len(dataset) * 0.65)
trainlist = dataset[:train_size]
testlist = dataset[train_size:]
def create_dataset(dataset, look_back):
#这里的look_back与timestep相同
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back)]
dataX.append(a)
dataY.append(dataset[i + look_back])
return numpy.array(dataX),numpy.array(dataY)
#训练数据太少 look_back并不能过大
look_back = 1
trainX,trainY = create_dataset(trainlist,look_back)
testX,testY = create_dataset(testlist,look_back)
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1] ,1 ))
# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(None,1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
# model.save(os.path.join("DATA","Test" + ".h5"))
# make predictions
#模型验证
#model = load_model(os.path.join("DATA","Test" + ".h5"))
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
#反归一化
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform(trainY)
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform(testY)
plt.plot(trainY)
plt.plot(trainPredict[1:])
plt.show()s
plt.plot(testY)
plt.plot(testPredict[1:])
plt.show()
预测未来数据方法
.单维单步（使用前n（2，代码演示为3）步预测后一步）
#对全部数据进行训练
import numpy
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential, load_model
dataset = df.values
# 将整型变为float
dataset = dataset.astype('float32')
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
def create_dataset(dataset, look_back):
#这里的look_back与timestep相同
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back)]
dataX.append(a)
dataY.append(dataset[i + look_back])
return numpy.array(dataX),numpy.array(dataY)
#训练数据太少 look_back并不能过大
look_back = 1
trainX,trainY = create_dataset(dataset,look_back)
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
# create and fit the LSTM network
model = Sequential()
model.add(LSTM(2, input_shape=(None,1)))
model.add(Dense(1))
model.compile(loss='mse', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
######################滚动预测########
predict_xlist = []#添加预测x列表
predict_y = []#添加预测y列表
timesteps=1
length=12
predict_xlist.extend(dataset[dataset.shape[0]-timesteps:dataset.shape[0],0].tolist())#已经存在的最后timesteps个数据添加进列表，预测新值(比如已经有的数据从1,2,3到288。现在要预测后面的数据，所以将216到288的72个数据添加到列表中，预测新的值即288以后的数据）
while len(predict_y) < length:
predictx = np.array(predict_xlist[-timesteps:])#从最新的predict_xlist取出timesteps个数据，预测新的predict_steps个数据（因为每次预测的y会添加到predict_xlist列表中，为了预测将来的值，所以每次构造的x要取这个列表中最后的timesteps个数据词啊性）
predictx = np.reshape(predictx,(1,timesteps,1))#变换格式，适应LSTM模型
#print("predictx"),print(predictx),print(predictx.shape)
#预测新值
lstm_predict = model.predict(predictx)
#predict_list.append(train_predict)#新值y添加进列表，做x
#滚动预测
#print("lstm_predict"),print(lstm_predict[0])
predict_xlist.extend(lstm_predict[0])#将新预测出来的predict_steps个数据，加入predict_xlist列表，用于下次预测
# invert
lstm_predict = scaler.inverse_transform(lstm_predict)
predict_y.extend(lstm_predict[0])#预测的结果y，每次预测的12个数据，添加进去，直到预测288个为止
单维多步（使用前两步预测后两步）
from numpy import array
# split a univariate sequence into samples
def split_sequence(sequence, n_steps_in, n_steps_out):
X, y = list(), list()
for i in range(len(sequence)):
# find the end of this pattern
end_ix = i + n_steps_in
out_end_ix = end_ix + n_steps_out
# check if we are beyond the sequence
if out_end_ix > len(sequence):
break
# gather input and output parts of the pattern
seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]
# 也就是用之前的三步，预测之后的2步
n_steps_in, n_steps_out = 3, 2
X, y = split_sequence(raw_seq, n_steps_in, n_steps_out)
for i in range(len(X)):
print(X[i], y[i])
'''
[10 20 30] [40 50]
[20 30 40] [50 60]
[30 40 50] [60 70]
[40 50 60] [70 80]
[50 60 70] [80 90]
'''
# reshape from [samples, timesteps] into [samples, timesteps, features]
n_features = 1
X = X.reshape((X.shape[0], X.shape[1], n_features))
# define model
model = Sequential()
model.add(LSTM(100, activation='relu', return_sequences=True, input_shape=(n_steps_in, n_features)))
model.add(LSTM(100, activation='relu'))
# 和之前单个步不同点在于 这一层的神经元=n_steps_out
model.add(Dense(n_steps_out))
model.compile(optimizer='adam', loss='mse')
model.fit(X, y, epochs=50, verbose=0)
# demonstrate prediction
x_input = array([70, 80, 90])
x_input = x_input.reshape((1, n_steps_in, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat) # [[107.168686 118.79841 ]]
多变量时间序列预测
#######################################多变量，多步骤预测
from numpy import array
from numpy import hstack
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
# split a multivariate sequence into samples
def split_sequences(sequences, n_steps_in, n_steps_out):
X, y = list(), list()
for i in range(len(sequences)):
# find the end of this pattern
end_ix = i + n_steps_in
out_end_ix = end_ix + n_steps_out-1
# check if we are beyond the dataset
if out_end_ix > len(sequences):
break
# gather input and output parts of the pattern
seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
# define input sequence
in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95])
out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])
# convert to [rows, columns] structure
in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))
# horizontally stack columns
dataset = hstack((in_seq1, in_seq2, out_seq))
# choose a number of time steps
n_steps_in, n_steps_out = 3, 2
# covert into input/output
X, y = split_sequences(dataset, n_steps_in, n_steps_out)
# the dataset knows the number of features, e.g. 2
n_features = X.shape[2]
# define model
model = Sequential()
model.add(LSTM(100, activation='relu', return_sequences=True, input_shape=(n_steps_in, n_features)))
model.add(LSTM(100, activation='relu'))
model.add(Dense(n_steps_out))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=200, verbose=0)
# demonstrate prediction
x_input = array([[70, 75], [80, 85], [90, 95]])
x_input = x_input.reshape((1, n_steps_in, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)
############################################################多变量，多对多
'''
[[10 15 25]
[20 25 45]
[30 35 65]] [[ 40 45 85]
[ 50 55 105]]-----y(输出为2*3维)
[[20 25 45]
[30 35 65]
[40 45 85]] [[ 50 55 105]
[ 60 65 125]]-----y
[[ 30 35 65]
[ 40 45 85]
[ 50 55 105]] [[ 60 65 125]
[ 70 75 145]]
[[ 40 45 85]
[ 50 55 105]
[ 60 65 125]] [[ 70 75 145]
[ 80 85 165]]
[[ 50 55 105]
[ 60 65 125]
[ 70 75 145]] [[ 80 85 165]
[ 90 95 185]]
'''
from numpy import array
from numpy import hstack
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import RepeatVector
from keras.layers import TimeDistributed
# split a multivariate sequence into samples
def split_sequences(sequences, n_steps_in, n_steps_out):
X, y = list(), list()
for i in range(len(sequences)):
# find the end of this pattern
end_ix = i + n_steps_in
out_end_ix = end_ix + n_steps_out
# check if we are beyond the dataset
if out_end_ix > len(sequences):
break
# gather input and output parts of the pattern
seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
# define input sequence
in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95])
out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])
# convert to [rows, columns] structure
in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))
# horizontally stack columns
dataset = hstack((in_seq1, in_seq2, out_seq))
# choose a number of time steps
n_steps_in, n_steps_out = 3, 2
# covert into input/output
X, y = split_sequences(dataset, n_steps_in, n_steps_out)
# the dataset knows the number of features, e.g. 2
n_features = X.shape[2]
# define model
model = Sequential()
model.add(LSTM(200, activation='relu', input_shape=(n_steps_in, n_features)))
model.add(RepeatVector(n_steps_out))
model.add(LSTM(200, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(n_features)))
'''
这里输入3D为（样本量，return_sequences数量（=n_steps_out）,200）
输出为（样本量，return_sequences数量（=n_steps_out），n_features）
就是每个输出是（3,2)维度的
'''
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=300, verbose=0)
# demonstrate prediction
x_input = array([[60, 65, 125], [70, 75, 145], [80, 85, 165]])
x_input = x_input.reshape((1, n_steps_in, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)
'''
总结：注意的点：
最大不同在DENSE的输出层，其实在多步骤的时候什么时候用n_featues,什么时候用n_steps还是看y的格式鸭
'''.多维单步（使用前三步去预测后一步）
from numpy import array
from numpy import hstack
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
# define input sequence
in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95])
out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])
# convert to [rows, columns] structure
in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))
# horizontally stack columns
dataset = hstack((in_seq1, in_seq2, out_seq))
print(dataset)
'''
[[ 10 15 25]
[ 20 25 45]
[ 30 35 65]
[ 40 45 85]
[ 50 55 105]
[ 60 65 125]
[ 70 75 145]
[ 80 85 165]
[ 90 95 185]]
现在定为时间步长为3
举个例子，就是用
10 15
20 25
30 35
预测65
'''
# split a multivariate sequence into samples
def split_sequences(sequences, n_steps):
X, y = list(), list()
for i in range(len(sequences)):
end_ix = i + n_steps
if end_ix > len(sequences):
break
seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] # 和单变量最大的不同在这里
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
n_steps = 3
X,y = split_sequences(dataset,n_steps)
print(X.shape,y.shape)
for i in range(len(X)):
print(X[i],y[i])
n_features = X.shape[2]
'''
[[10 15]
[20 25]
[30 35]] 65
[[20 25]
[30 35]
[40 45]] 85
[[30 35]
[40 45]
[50 55]] 105
[[40 45]
[50 55]
[60 65]] 125
[[50 55]
[60 65]
[70 75]] 145
[[60 65]
[70 75]
[80 85]] 165
[[70 75]
[80 85]
[90 95]] 185
'''
model = Sequential()
model.add(LSTM(50,activation='relu',input_shape=(n_steps,n_features)))
model.add(Dense(1))
model.compile(optimizer='adam',loss='mse')
model.fit(X,y,epochs=200,verbose=0)
#预测模型
x_input = array([[80, 85], [90, 95], [100, 105]])
x_input = x_input.reshape((1, n_steps, n_features)) # 转换成样本量+步长+特征的格式
yhat = model.predict(x_input, verbose=0)
print(yhat) # 209.00851
多维多步的预测（使用前三步去预测后两步）
from numpy import array
from numpy import hstack
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
# split a multivariate sequence into samples
def split_sequences(sequences, n_steps_in, n_steps_out):
X, y = list(), list()
for i in range(len(sequences)):
# find the end of this pattern
end_ix = i + n_steps_in
out_end_ix = end_ix + n_steps_out-1
# check if we are beyond the dataset
if out_end_ix > len(sequences):
break
# gather input and output parts of the pattern
seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
# define input sequence
in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95])
out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])
# convert to [rows, columns] structure
in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))
# horizontally stack columns
dataset = hstack((in_seq1, in_seq2, out_seq))
# choose a number of time steps
n_steps_in, n_steps_out = 3, 2
# covert into input/output
X, y = split_sequences(dataset, n_steps_in, n_steps_out)
# the dataset knows the number of features, e.g. 2
n_features = X.shape[2]
# define model
model = Sequential()
model.add(LSTM(100, activation='relu', return_sequences=True, input_shape=(n_steps_in, n_features)))
model.add(LSTM(100, activation='relu'))
model.add(Dense(n_steps_out))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=200, verbose=0)
# demonstrate prediction
x_input = array([[70, 75], [80, 85], [90, 95]])
x_input = x_input.reshape((1, n_steps_in, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)
############################################################多变量，多对多
'''
[[10 15 25]
[20 25 45]
[30 35 65]] [[ 40 45 85]
[ 50 55 105]]-----y(输出为2*3维)
[[20 25 45]
[30 35 65]
[40 45 85]] [[ 50 55 105]
[ 60 65 125]]-----y
[[ 30 35 65]
[ 40 45 85]
[ 50 55 105]] [[ 60 65 125]
[ 70 75 145]]
[[ 40 45 85]
[ 50 55 105]
[ 60 65 125]] [[ 70 75 145]
[ 80 85 165]]
[[ 50 55 105]
[ 60 65 125]
[ 70 75 145]] [[ 80 85 165]
[ 90 95 185]]
'''
from numpy import array
from numpy import hstack
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import RepeatVector
from keras.layers import TimeDistributed
# split a multivariate sequence into samples
def split_sequences(sequences, n_steps_in, n_steps_out):
X, y = list(), list()
for i in range(len(sequences)):
# find the end of this pattern
end_ix = i + n_steps_in
out_end_ix = end_ix + n_steps_out
# check if we are beyond the dataset
if out_end_ix > len(sequences):
break
# gather input and output parts of the pattern
seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
# define input sequence
in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95])
out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])
# convert to [rows, columns] structure
in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))
# horizontally stack columns
dataset = hstack((in_seq1, in_seq2, out_seq))
# choose a number of time steps
n_steps_in, n_steps_out = 3, 2
# covert into input/output
X, y = split_sequences(dataset, n_steps_in, n_steps_out)
# the dataset knows the number of features, e.g. 2
n_features = X.shape[2]
# define model
model = Sequential()
model.add(LSTM(200, activation='relu', input_shape=(n_steps_in, n_features)))
model.add(RepeatVector(n_steps_out))
model.add(LSTM(200, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(n_features)))
'''
这里输入3D为（样本量，return_sequences数量（=n_steps_out）,200）
输出为（样本量，return_sequences数量（=n_steps_out），n_features）
就是每个输出是（3,2)维度的
'''
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=300, verbose=0)
# demonstrate prediction
x_input = array([[60, 65, 125], [70, 75, 145], [80, 85, 165]])
x_input = x_input.reshape((1, n_steps_in, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)
'''
总结：注意的点：
最大不同在DENSE的输出层，其实在多步骤的时候什么时候用n_featues,什么时候用n_steps还是看y的格式鸭