2023.3.5

import sys
sys.path.append('../code') # 设置路径
import numpy as np
import pandas as pd
from GM11 import GM11# 引入自编的灰色预测函数
import matplotlib.pyplot as plt

inputfile2 = "C:\\Users\\ASUS\\Documents\\WeChat Files\\wxid_ivbyuelp335q22\\FileStorage\\File\\2023-02\\data.csv " # 输入的数据文件
inputfile1 = "C:\\Users\\ASUS\\Documents\\WeChat Files\\wxid_ivbyuelp335q22\\FileStorage\\File\\2023-02\\new_reg_data.csv " # 输入的数据文件
new_reg_data = pd.read_csv(inputfile1) # 读取经过特征选择后的数据
data = pd.read_csv(inputfile2) # 读取总的数据
new_reg_data.index = range(1994, 2014)
new_reg_data.loc[2014] = None
new_reg_data.loc[2015] = None
new_reg_data.loc[2016]=None
l = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']

for i in l:
f = GM11(new_reg_data.loc[range(1994, 2014),i].values)[0]
new_reg_data.loc[2014,i] = f(len(new_reg_data)-1) # 2014年预测结果
new_reg_data.loc[2015,i] = f(len(new_reg_data)) # 2015年预测结果
new_reg_data.loc[2016,i]=f(len(new_reg_data)+1)
new_reg_data[i] = new_reg_data[i].round(2) # 保留两位小数
outputfile="C:\\Users\\ASUS\\Documents\\WeChat Files\\wxid_ivbyuelp335q22\\FileStorage\\File\\2023-02\\new_reg_data_GM11.xlsx"
y = list(data['y'].values) # 提取财政收入列，合并至新数据框中
y.extend([np.nan,np.nan,np.nan])
new_reg_data['y'] = y
new_reg_data.to_excel(outputfile) # 结果输出
plt.title('3137',fontsize=20)
print('预测结果为：\n',new_reg_data.loc[2014:2016,:]) # 预测结果展示

import matplotlib.pyplot as plt
from sklearn.svm import LinearSVR

inputfile = "C:\\Users\\ASUS\\Documents\\WeChat Files\\wxid_ivbyuelp335q22\\FileStorage\\File\\2023-02\\new_reg_data_GM11.xlsx" # 灰色预测后保存的路径
data = pd.read_excel(inputfile) # 读取数据
feature = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13'] # 属性所在列
data.index = range(1994,2017)
data_train = data.loc[range(1994,2014)].copy() # 取2014年前的数据建模
data_mean = data_train.mean()
data_std = data_train.std()
data_train = (data_train - data_mean)/data_std # 数据标准化
x_train = data_train[feature].values # 属性数据
y_train = data_train['y'].values # 标签数据

linearsvr = LinearSVR() # 调用LinearSVR()函数
linearsvr.fit(x_train,y_train)
x = ((data[feature] - data_mean[feature])/data_std[feature]).values # 预测，并还原结果。
data['y_pred'] = linearsvr.predict(x) * data_std['y'] + data_mean['y']
outputfile = "C:\\Users\\ASUS\\Documents\\WeChat Files\\wxid_ivbyuelp335q22\\FileStorage\\File\\2023-02\\new_reg_data_GM11_revenue.xls" # SVR预测后保存的结果
data.to_excel(outputfile)
print('真实值与预测值分别为：\n',data[['y','y_pred']])
fig = data[['y','y_pred']].plot(subplots = True, style=['b-o','r-*'])# 画出预测结果图
plt.show()