时序预测-基于支持向量机SVR的时间序列预测Python程序

# -*- coding: utf-8 -*- """ Created on Thu Aug 8 10:18:36 2019 @author: Admini """ import xlrd import matplotlib.pyplot as plt def read_20180829(): fname = "data.xlsx" bk = xlrd.open_workbook(fname) # shxrange = range(bk.nsheets) try: sh = bk.sheet_by_name("Sheet1") except: print("no sheet in %s named Sheet1" % fname) # 获取行数 nrows = sh.nrows # 获取列数 ncols = sh.ncols print("nrows %d, ncols %d" % (nrows, ncols)) # 获取第一行第一列数据 cell_value = sh.cell_value(1, 0) print(cell_value) time = [] single1 = [] single2 = [] single3 = [] # 获取各行数据 for i in range(1, nrows): row_data = sh.cell_value(i, 0) # print('time', row_data) time.append(row_data) for i in range(1, nrows): row_data = sh.cell_value(i, 1) # print('a', row_data) single1.append(row_data) for i in range(1, nrows): row_data = sh.cell_value(i, 2) # print('a', row_data) single2.append(row_data) for i in range(1, nrows): row_data = sh.cell_value(i, 3) # print('a', row_data) single3.append(row_data) return time,single1,single2,single3 time,single1,single2,single3 = read_20180829() plt.subplot(2, 2, 1) plt.plot(time, single1) plt.xlabel("time") plt.ylabel("single1") plt.subplot(2, 2, 2) plt.plot(time, single2) plt.xlabel("time") plt.ylabel("single2") plt.subplot(2, 2, 3) plt.plot(time, single3) plt.xlabel("time") plt.ylabel("single3") plt.show() #time时间列 single1 信号值 取前多少个X_data预测下一个数据 def time_slice(time,single,X_lag): sample = [] label = [] for k in range(len(time) - X_lag - 1): t = k + X_lag sample.append(single[k:t]) label.append(single[t + 1]) return sample,label sample,label = time_slice(time,single1,5) from sklearn.externals import joblib from sklearn.grid_search import GridSearchCV from sklearn import svm from sklearn.model_selection import train_test_split from sklearn.utils import shuffle import numpy as np #数据集划分 X_train, X_test, y_train, y_test = train_test_split(sample, label, test_size=0.3, random_state=42) #数据集掷乱 random_seed = 13 X_train, y_train = shuffle(X_train, y_train, random_state=random_seed) #参数设置SVR准备 parameters = {'kernel':['rbf'], 'gamma':np.logspace(-5, 0, num=6, base=2.0),'C':np.logspace(-5, 5, num=11, base=2.0)} #网格搜索：选择十折交叉验证 svr = svm.SVR() grid_search = GridSearchCV(svr, parameters, cv=10, n_jobs=4, scoring='mean_squared_error') #SVR模型训练 grid_search.fit(X_train,y_train) #输出最终的参数 print(grid_search.best_params_) #模型的精度 print(grid_search.best_score_) #SVR模型保存 joblib.dump(grid_search,'svr.pkl') #SVR模型加载 svr=joblib.load('svr.pkl') #SVR模型测试 y_hat = svr.predict(X_test) #计算预测值与实际值的残差绝对值 abs_vals= np.abs(y_hat-y_test) import matplotlib.pyplot as plt plt.subplot(2, 1, 1) plt.plot(y_test,c='k',label='data') plt.plot(y_hat,c='g',label='svr model') plt.xlabel('data') plt.ylabel('target') plt.title('Support Vector Regression') plt.legend() plt.show() plt.subplot(2, 2, 1) plt.plot(abs_vals) plt.show()