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问题:

I want to perform time-series prediction of future events using SVR module from scikit-learn. Here is my source code I am trying to work with:

import csv import numpy as np from sklearn.svm import SVR import matplotlib.pyplot as plt   plt.switch_backend('newbackend')  seq_num=[] win=[]  def get_data(filename):     with open(filename, 'r') as csvfile:         csvFileReader = csv.reader(csvfile)         next(csvFileReader) # skipping column names         for row in csvFileReader:             seq_num.append(int(row[0])             win.append(int(row[6]))     return  def predict_win(X, y, x):     win = np.reshape(X,(len(X), 1))       svr_lin = SVR(kernel= 'linear', C= 1e3)     svr_poly = SVR(kernel= 'poly', C= 1e3, degree= 2)     svr_rbf = SVR(kernel= 'rbf', C= 1e3, gamma= 0.1)      svr_rbf.fit(X, y)      svr_lin.fit(X, y)     svr_poly.fit(X, y)      plt.scatter(X, y, color= 'black', label= 'Data')      plt.plot(y, svr_rbf.predict(X), color= 'red', label= 'RBF model')      plt.plot(y,svr_lin.predict(X), color= 'green', label= 'Linear model')      plt.plot(y,svr_poly.predict(X), color= 'blue', label= 'Polynomial model')      plt.xlabel('X, other features')     plt.ylabel('win')     plt.title('Support Vector Regression')     plt.legend()     plt.show()      return svr_rbf.predict(x)[0], svr_lin.predict(x)[0], svr_poly.predict(x)[0]  get_data('net_data.csv')    predicted_win = predict_win(X, y, 29)

My dataset is very huge and so a portion of my csv dataset is included at the end. I am interested in the 7th column. I wanted to predict when the values in the 7th column increase or when it decreases. Is it possible to look into the 7th column ONLY and do the time-series prediction? Any help with this would be so much appreciated? Thanks!

0.007804347,10.0.0.11:49438,10.0.12.12:5001,32,3796291040,3796277984,10,2147483647,28960,3034,29312 0.007856739,10.0.0.11:49438,10.0.12.12:5001,32,3796293936,3796278008,11,2147483647,29056,2999,29312 0.010605189,10.0.0.11:49438,10.0.12.12:5001,32,3796320000,3796291040,20,2147483647,55040,2969,29312 0.010850907,10.0.0.11:49438,10.0.12.12:5001,32,3796348960,3796305520,30,2147483647,84096,2946,29312 0.013598458,10.0.0.11:49438,10.0.12.12:5001,32,3796377920,3796320000,40,2147483647,113024,2951,29312 0.01368011,10.0.0.11:49438,10.0.12.12:5001,32,3796434392,3796348960,60,2147483647,170880,2956,29312 0.015104265,10.0.0.11:49438,10.0.12.12:5001,32,3796434392,3796363440,70,2147483647,199936,2940,29312 0.016406964,10.0.0.11:49438,10.0.12.12:5001,32,3796490864,3796377920,80,2147483647,220160,2943,29312 0.016465876,10.0.0.11:49438,10.0.12.12:5001,32,3796537200,3796432944,81,80,330240,2925,29312 0.018355321,10.0.0.11:49438,10.0.12.12:5001,32,3796547336,3796434392,81,80,333056,2914,29312 0.020171945,10.0.0.11:49438,10.0.12.12:5001,32,3796603808,3796490864,83,80,382336,2956,29312 0.237314523,10.0.0.11:49438,10.0.12.12:5001,32,3810417728,3809658976,529,396,1775360,7109,29312 0.237409075,10.0.0.11:49438,10.0.12.12:5001,44,3810417728,3809700968,530,397,1859328,7381,29312 0.237486647,10.0.0.11:49438,10.0.12.12:5001,44,3810417728,3809700968,371,371,1960704,7365,29312 0.237807596,10.0.0.11:49438,10.0.12.12:5001,44,3810417728,3809700968,371,371,1980928,7362,29312 0.237989588,10.0.0.11:49438,10.0.12.12:5001,44,3810417728,3809700968,371,371,1989632,7400,29312 0.259123971,10.0.0.11:49438,10.0.12.12:5001,32,3811590608,3811251776,261,260,2267648,5885,29312 0.259174008,10.0.0.11:49438,10.0.12.12:5001,32,3811655768,3811289424,261,260,2267648,5918,29312 0.262546461,10.0.0.11:49438,10.0.12.12:5001,32,3811720928,3811354584,261,260,2267648,5823,29312

回答1:

Ok, the svm function below has a problem:

The second line, win = ... is unused, and will cause an error. Delete it.

def predict_win(X, y, x):     win = np.reshape(X,(len(X), 1))  #

Second, I don't know why there is an entire function for reading a csv. Ignore it and use pandas. Here is a sample code that will work:

from sklearn import svm import pandas as pd import numpy as np import matplotlib.pyplot as plt  def predict_win(X,y,x):     svr_lin = svm.SVR(kernel='linear',C=1e3)     svr_poly = svm.SVR(kernel='poly',C=1e3, degree=2)     svr_rbf = svm.SVR(kernel='rbf',C=1e3,gamma=0.1)     svr_rbf.fit(X,y)     svr_lin.fit(X,y)     svr_poly.fit(X,y)      plt.plot(y,svr_rbf.predict(X),color='red',label='RBF model')     plt.plot(y,svr_lin.predict(X),color='green',label='Linear model')     plt.plot(y,svr_poly.predict(X),color='blue', label='Polynomial model')     plt.xlabel('X, other features')     plt.ylabel('win')     plt.title('Support Vector Regression')     plt.legend()     plt.show()     return [svr_rbf.predict(x)[0],svr_lin.predict(x)[0],svr_poly.predict(x)[0]]  df = pd.read_csv('data.csv')  data_np_array = df.values  y = np.ndarray.copy(data_np_array[:,6]) Xleft = np.ndarray.copy(data_np_array[:,:6]) Xright = np.ndarray.copy(data_np_array[:,7:]) X = np.hstack((Xleft,Xright))  x0 = np.ndarray.copy(X[0,:]) xp = predict_win(X,y,x0)  percent_off = [min(data_np_array[0,2],prediction)/max(data_np_array[0,2],prediction) for prediction in xp]

The intermediate steps, where you clean up the imported data, turn it from a dataframe to a numpy array, copy your 7th column as the regression to fit, delete it from your training data, and rebuild a new array must be done before fitting to the SVR.

df = pd.read_csv('data.csv')  data_np_array = df.values  y = np.ndarray.copy(data_np_array[:,6]) Xleft = np.ndarray.copy(data_np_array[:,:6]) Xright = np.ndarray.copy(data_np_array[:,7:]) X = np.hstack((Xleft,Xright))

Let me know if these worked. I just took a few lines from your data table above.

文章来源: Prediction: Time-series prediction of future events using SVR module

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