How can I make the r2 to be positive when predicting Bitcoin price using SVR?
0
I try to use SVR to predict Bitcoin price and I include more than 30 variables, but the prediction result is poor. How can I improve it or at least make the R2 to be positive?
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
import pandas as pd
from sklearn import preprocessing
from sklearn.metrics import mean_squared_error,r2_score
import numpy as np
def mean_absolute_percentage_error(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.mean(np.abs((y_true - y_pred) / y_true))
data=pd.read_excel(r'X:final.xlsx')
start_train='2014-01-01'
end_train='2017-12-31'
start_test='2018-01-01'
end_test='2018-12-31'
data=data.set_index('Date')
data_train=data.ix[start_train:end_train]
x_columns=list(data.drop((['next_price']),axis=1).columns)
y_column='next_price'
x_train=data_train[x_columns]
y_train=data_train[y_column]
x_train = preprocessing.scale(x_train)
data_test=data.ix[start_test:end_test]
x_test=data_test[x_columns]
y_test=data_test[y_column]
x_test = preprocessing.scale(x_test)
svr=SVR(kernel='linear')
svr.fit(x_train,y_train)
predictions=svr.predict(x_test)
print('MAPE: {0:.3f}'.format(mean_absolute_percentage_error(y_test,predictions)))
print('RMSE: {0:.3f}'.format((mean_squared_error(y_test,predictions))**0.5))
print('R^2: {0:.3f}'.format(r2_score(y_test,predictions)))
MAPE: 0.889;
RMSE: 7836.500;
R^2: -11.642
dates=data_test.index.values
plot_truth,=plt.plot(dates,y_test,'k')
plot_svr,=plt.plot(dates,predictions,'g')
plt.title('Real price VS Predicted price')
plt.show()
![<code>[enter image description here][1]</code>](https://i.stack.imgur.com/EjeB4.png)
The original data is here[1]: https://drive.google.com/open?id=1Na0WB5zrCbafb4xnBM9ggRsm8p1LsvXi
svm bitcoin
asked Nov 23 '18 at 7:54
wwhywwhy
347
add a comment |
0
I try to use SVR to predict Bitcoin price and I include more than 30 variables, but the prediction result is poor. How can I improve it or at least make the R2 to be positive?
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
import pandas as pd
from sklearn import preprocessing
from sklearn.metrics import mean_squared_error,r2_score
import numpy as np
def mean_absolute_percentage_error(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.mean(np.abs((y_true - y_pred) / y_true))
data=pd.read_excel(r'X:final.xlsx')
start_train='2014-01-01'
end_train='2017-12-31'
start_test='2018-01-01'
end_test='2018-12-31'
data=data.set_index('Date')
data_train=data.ix[start_train:end_train]
x_columns=list(data.drop((['next_price']),axis=1).columns)
y_column='next_price'
x_train=data_train[x_columns]
y_train=data_train[y_column]
x_train = preprocessing.scale(x_train)
data_test=data.ix[start_test:end_test]
x_test=data_test[x_columns]
y_test=data_test[y_column]
x_test = preprocessing.scale(x_test)
svr=SVR(kernel='linear')
svr.fit(x_train,y_train)
predictions=svr.predict(x_test)
print('MAPE: {0:.3f}'.format(mean_absolute_percentage_error(y_test,predictions)))
print('RMSE: {0:.3f}'.format((mean_squared_error(y_test,predictions))**0.5))
print('R^2: {0:.3f}'.format(r2_score(y_test,predictions)))
MAPE: 0.889;
RMSE: 7836.500;
R^2: -11.642
dates=data_test.index.values
plot_truth,=plt.plot(dates,y_test,'k')
plot_svr,=plt.plot(dates,predictions,'g')
plt.title('Real price VS Predicted price')
plt.show()
The original data is here[1]: https://drive.google.com/open?id=1Na0WB5zrCbafb4xnBM9ggRsm8p1LsvXi
svm bitcoin
asked Nov 23 '18 at 7:54
wwhywwhy
347
add a comment |
0
0
0
I try to use SVR to predict Bitcoin price and I include more than 30 variables, but the prediction result is poor. How can I improve it or at least make the R2 to be positive?
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
import pandas as pd
from sklearn import preprocessing
from sklearn.metrics import mean_squared_error,r2_score
import numpy as np
def mean_absolute_percentage_error(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.mean(np.abs((y_true - y_pred) / y_true))
data=pd.read_excel(r'X:final.xlsx')
start_train='2014-01-01'
end_train='2017-12-31'
start_test='2018-01-01'
end_test='2018-12-31'
data=data.set_index('Date')
data_train=data.ix[start_train:end_train]
x_columns=list(data.drop((['next_price']),axis=1).columns)
y_column='next_price'
x_train=data_train[x_columns]
y_train=data_train[y_column]
x_train = preprocessing.scale(x_train)
data_test=data.ix[start_test:end_test]
x_test=data_test[x_columns]
y_test=data_test[y_column]
x_test = preprocessing.scale(x_test)
svr=SVR(kernel='linear')
svr.fit(x_train,y_train)
predictions=svr.predict(x_test)
print('MAPE: {0:.3f}'.format(mean_absolute_percentage_error(y_test,predictions)))
print('RMSE: {0:.3f}'.format((mean_squared_error(y_test,predictions))**0.5))
print('R^2: {0:.3f}'.format(r2_score(y_test,predictions)))
MAPE: 0.889;
RMSE: 7836.500;
R^2: -11.642
dates=data_test.index.values
plot_truth,=plt.plot(dates,y_test,'k')
plot_svr,=plt.plot(dates,predictions,'g')
plt.title('Real price VS Predicted price')
plt.show()
The original data is here[1]: https://drive.google.com/open?id=1Na0WB5zrCbafb4xnBM9ggRsm8p1LsvXi
svm bitcoin
asked Nov 23 '18 at 7:54
wwhywwhy
347
I try to use SVR to predict Bitcoin price and I include more than 30 variables, but the prediction result is poor. How can I improve it or at least make the R2 to be positive?
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
import pandas as pd
from sklearn import preprocessing
from sklearn.metrics import mean_squared_error,r2_score
import numpy as np
def mean_absolute_percentage_error(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.mean(np.abs((y_true - y_pred) / y_true))
data=pd.read_excel(r'X:final.xlsx')
start_train='2014-01-01'
end_train='2017-12-31'
start_test='2018-01-01'
end_test='2018-12-31'
data=data.set_index('Date')
data_train=data.ix[start_train:end_train]
x_columns=list(data.drop((['next_price']),axis=1).columns)
y_column='next_price'
x_train=data_train[x_columns]
y_train=data_train[y_column]
x_train = preprocessing.scale(x_train)
data_test=data.ix[start_test:end_test]
x_test=data_test[x_columns]
y_test=data_test[y_column]
x_test = preprocessing.scale(x_test)
svr=SVR(kernel='linear')
svr.fit(x_train,y_train)
predictions=svr.predict(x_test)
print('MAPE: {0:.3f}'.format(mean_absolute_percentage_error(y_test,predictions)))
print('RMSE: {0:.3f}'.format((mean_squared_error(y_test,predictions))**0.5))
print('R^2: {0:.3f}'.format(r2_score(y_test,predictions)))
MAPE: 0.889;
RMSE: 7836.500;
R^2: -11.642
dates=data_test.index.values
plot_truth,=plt.plot(dates,y_test,'k')
plot_svr,=plt.plot(dates,predictions,'g')
plt.title('Real price VS Predicted price')
plt.show()
The original data is here[1]: https://drive.google.com/open?id=1Na0WB5zrCbafb4xnBM9ggRsm8p1LsvXi
svm bitcoin
svm bitcoin
asked Nov 23 '18 at 7:54
wwhywwhy
347
asked Nov 23 '18 at 7:54
wwhywwhy
347
asked Nov 23 '18 at 7:54
wwhywwhy
347
asked Nov 23 '18 at 7:54
wwhywwhy
347
asked Nov 23 '18 at 7:54
wwhywwhy
347
347
add a comment |
add a comment |
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