1、数据概况

数据十分简单，就只有日期，以及对应的销量。

?2、代码

本次我使用jupyter notebook 来整，，主要是可以更方便的看出每组代码的输出结果。代码如下，

#导入相关库
import numpy
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
import  pandas as pd
import  os
from keras.models import Sequential, load_model
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.preprocessing import MinMaxScaler

然后进行数据导入和相关处理

dataframe = pd.read_csv(r"D:\桌面文件夹哦\数据统计\悠度数据\2特征.CSV", usecols=[1], engine='python', skipfooter=3)
dataset = dataframe.values
# 将整型变为float
dataset = dataset.astype('float32')
#归一化
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
train_size = int(len(dataset) * 0.80)
trainlist = dataset[:train_size]
testlist = dataset[train_size:]

定义一个划分测试集和训练集的函数

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 ))

拟合并保存模型

# create and fit the LSTM network
model = Sequential()
model.add(LSTM(5, input_shape=(None,1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=2, verbose=2)
model.save("D:\桌面文件夹哦\预测模型保存")#模型保存位置

加载保存好的模型，进行测试（将训练集，测试集数据输入，输出对应预测结果，对输出的结果进行反归一化，再与真实的训练测试数据进行对比）

model = load_model(r"D:\桌面文件夹哦\预测模型保存")
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)

将预测的结果与真实结果进行作图

plt.plot(trainY)
plt.plot(trainPredict[1:])
plt.show()
plt.plot(testY)
plt.plot(testPredict[1:])
plt.show()

?最后用r2、rmse等指标来衡量预测结果

r_2 = r2_score(testPredict,testY)
print('Test r_2: %.3f' % r_2)
# 计算MAE
mae = mean_absolute_error(testPredict,testY)
print('Test MAE: %.3f' % mae)
# 计算RMSE
from math import sqrt
rmse = sqrt(mean_squared_error(testPredict,testY))
print('Test RMSE: %.3f' % rmse)

预测结果如下?