首先我们需要一些数据来对其进行预测：
第0列是我们的序列号，single1，single2，single3是我们需要预测的数据，我们首先需要对其进行读取：
def read_20180829(): fname = "20180829.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 # 获取第一行第一列数据 cell_value = sh.cell_value(1, 0) time = [] single1 = [] single2 = [] single3 = [] # 获取各行数据 for i in range(1, nrows): row_data = sh.cell_value(i, 0) time.append(row_data) for i in range(1, nrows): row_data = sh.cell_value(i, 1) single1.append(row_data) for i in range(1, nrows): row_data = sh.cell_value(i, 2) single2.append(row_data) for i in range(1, nrows): row_data = sh.cell_value(i, 3) single3.append(row_data) return time,single1,single2,single3
得到数据之后我们就可以对其进行预测了：
import numpy as np from matplotlib import pyplot as plt from sklearn.svm import SVR from read_data import read_20180829 time,single1,single2,single3 = read_20180829() # 需要预测的长度是多少 long_predict = 40 def svm_timeseries_prediction(c_parameter,gamma_paramenter): X_data = time Y_data = single1 print(len(X_data)) # 整个数据的长度 long = len(X_data) # 取前多少个X_data预测下一个数据 X_long = 1 error = [] svr_rbf = SVR(kernel='rbf', C=c_parameter, gamma=gamma_paramenter) # svr_rbf = SVR(kernel='rbf', C=1e5, gamma=1e1) # svr_rbf = SVR(kernel='linear',C=1e5) # svr_rbf = SVR(kernel='poly',C=1e2, degree=1) X = [] Y = [] for k in range(len(X_data) - X_long - 1): t = k + X_long X.append(Y_data[k:t]) Y.append(Y_data[t + 1]) y_rbf = svr_rbf.fit(X[:-long_predict], Y[:-long_predict]).predict(X[:]) for e in range(len(y_rbf)): error.append(Y_data[X_long + 1 + e] - y_rbf[e]) return X_data,Y_data,X_data[X_long+1:],y_rbf,error X_data,Y_data,X_prediction,y_prediction,error = svm_timeseries_prediction(10,1) figure = plt.figure() tick_plot = figure.add_subplot(2, 1, 1) tick_plot.plot(X_data, Y_data, label='data', color='green', linestyle='-') tick_plot.axvline(x=X_data[-long_predict], alpha=0.2, color='gray') # tick_plot.plot(X_data[:-X_long-1], y_rbf, label='data', color='red', linestyle='--') tick_plot.plot(X_prediction, y_prediction, label='data', color='red', linestyle='--') tick_plot = figure.add_subplot(2, 1, 2) tick_plot.plot(X_prediction,error) plt.show()
相关数据、代码网址：https://github.com/ZhiqiangHo/code-of-csdn/tree/master/time_series_prediction/SVM
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