常用的LSTM时间序列预测（单变量时间序列预测 | 多变量时间序列预测 | 他们的预测未来数据方法）

单变量时间序列预测

数据类型：单列

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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