import?numpy
import?matplotlib.pyplot?as?plt
from?pandas?import?read_csv
import?math
from?keras.models?import?Sequential
from?keras.layers?import?Dense
from?keras.layers?import?LSTM
from?sklearn.preprocessing?import?MinMaxScaler
from?sklearn.metrics?import?mean_squared_error
import??pandas?as?pd
import??os
from?keras.models?import?Sequential?load_model
#?load?the?dataset
dataframe?=?read_csv（‘./international-airline-passengers.csv‘?usecols=[1]?engine=‘python‘?skipfooter=3）

dataset?=?dataframe.values
train_size?=?int（len（dataset）?*?0.65）
scaler?=?MinMaxScaler（feature_range=（0?1））
dataset?=?scaler.fit_transform（dataset）
trainlist?=?dataset[:train_size:]
testlist?=?dataset[train_size::]



#?X?is?the?number?of?passengers?at?a?given?time?（t）?and?Y?is?the?number?of?passengers?at?the?next?time?（t?+?1）.

#?convert?an?array?of?values?into?a?dataset?matrix
def?create_dataset（dataset?look_back）:
#這里的look_back與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）n