pythonscikit-learnsvmforecastingvolatility

SVM classifier n_samples, n_splits problem sklearn Python

I'm trying to predict volatility one step ahead with an SVM model based on O'Reilly book example (Machine Learning for Financial Risk Management with Python). When I copy exactly the example (with S&P500 data) it works well but now I'm having troubles with this chunk of code with a particular fund returns data:

# returns
r = np.array([        nan,  0.0013933 ,  0.00118874,  0.00076462,  0.00168565,
       -0.00018507, -0.00390753,  0.00307275, -0.00351472])

# horizon
t = 252

# mean of returns
mu = r.mean()

# critical value
z = norm.ppf(0.95)

# realized volatility
vol = r.rolling(5).std()
vol = pd.DataFrame(vol)
vol.reset_index(drop=True, inplace=True)

# SVM GARCH
r_svm = r ** 2
r_svm = r_svm.reset_index()

# inputs X (returns and realized volatility)
X = pd.concat([vol, r_svm], axis=1, ignore_index=True)
X = X.dropna().copy()
X = X.reset_index()
X.drop([1, 'index'], axis=1, inplace=True)

# labels y realized volatility shifted 1 period onward
vol = vol.dropna().reset_index()
vol.drop('index', axis=1, inplace=True)

# linear kernel
svr_lin = SVR(kernel='linear')

# hyperparameters grid
para_grid = {'gamma': sp_rand(),
'C': sp_rand(),
'epsilon': sp_rand()}

# svm classifier (regression?)
clf = RandomizedSearchCV(svr_lin, para_grid)
clf.fit(X[:-1].dropna().values,
vol[1:].values.reshape(-1,))

# prediction
n_vol = clf.predict(X.iloc[-1:])

The raised error is:

ValueError: Cannot have number of splits n_splits=5 greater than the number of samples: n_samples=3.

The code works with longer returns series so I assume that the problem is the length of the array but I can't figure out how to solve it. can someone help me with that?

Solution

  • This error is getting raised because you use RandomizedSearchCV with default cv parameter. By default RandomizedSearchCV is running 5-folds cross-validation to find the best hyperparameters for the model.

    5-folds cross-validation means splitting your training data into 5 subsets and training 5 different models based on these splits.

    Looks like you have less than 5 objects in your training set, so splitting your data into 5 folds isn't possible.

    To fix the issue you should either add more data or decrease number of folds for the RandomizedSearchCV by adding cv parameter:

    clf = RandomizedSearchCV(svr_lin, para_grid, cv=2)

    I'd recommend to collect more data, since 4 data points most likely won't be enough to make the model accurate or predictive.