you can access the data set at this link https://drive.google.com/file/d/0B9Hd-26lI95ZeVU5cDY0ZU5MTWs/view?usp=sharing
My Task is to predict the price movement of a sector fund. How much it goes up or down doesn't really matter, I only want to know whether it's going up or down. So I define it as a classification problem.
Since this data set is a time-series data, I met many problems. I have read articles about these problems like I can't use k-fold cross validation since this is time series data. You can't ignore the order of the data.
my code is as follows:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
from sklearn.linear_model import LinearRegression
from math import sqrt
from sklearn.svm import LinearSVC
from sklearn.svm import SVCenter code here
lag1 = pd.read_csv(#local file path, parse_dates=['Date'])
#Trend : if price going up: ture, otherwise false
lag1['Trend'] = lag1.XLF > lag1.XLF.shift()
train_size = round(len(lag1)*0.50)
train = lag1[0:train_size]
test = lag1[train_size:]
variable_to_use= ['rGDP','interest_rate','private_auto_insurance','M2_money_supply','VXX']
y_train = train['Trend']
X_train = train[variable_to_use]
y_test = test['Trend']
X_test = test[variable_to_use]
#SVM Lag1
this_C = 1.0
clf = SVC(kernel = 'linear', C=this_C).fit(X_train, y_train)
print('XLF Lag1 dataset')
print('Accuracy of Linear SVC classifier on training set: {:.2f}'
.format(clf.score(X_train, y_train)))
print('Accuracy of Linear SVC classifier on test set: {:.2f}'
.format(clf.score(X_test, y_test)))
#Check prediction results
clf.predict(X_test)
First of all, is my method right here : first generating a column of true and false? I am afraid the machine can't understand this column if I simply feed this column to it. Should I first perform a regression then compare the numeric result to generate a list of going up or down?
The accuracy on training set is very low at : 0.58 I am getting an array with all trues with clf.predict(X_test) which I don't know why I would get all trues.
And I don't know whether the resulting accuracy is calculated in which way: for example, I think my current accuracy only counts the number of true and false but ignoring the order of them? Since this is time-series data, ignoring the order is not right and gives me no information about predicting price movement. Let's say I have 40 examples in test set, and I got 20 Tures I would get 50% accuracy. But I guess the trues are no in the right position as it appears in the ground truth set. (Tell me if I am wrong)
I am also considering using Gradient Boosted Tree to do the classification, would it be better?