Multiple-output support vector regression is a method which implements support vector regression with multi-input and multi-output. This package is based on our paper Multi-step-ahead time series prediction using multiple-output support vector regression.
numpy
sklearn
from model.MSVR import MSVR
from model.utility import create_dataset, rmse
import numpy as np
# Construct x samples (input) and y samples (output)
# x: num_samples * inputDim
# y: num_smaples * outputH
ts = np.sin(np.arange(0, 9, 0.01)).reshape(-1)
segmentation = int(len(ts)*2/3)
dim = 50
h = 5
dataset = create_dataset(ts, dim, h)
X, Y = dataset[:, :(0 - h)], dataset[:, (0-h):]
train_input = X[:segmentation, :]
train_target = Y[:segmentation].reshape(-1, h)
test_input = X[segmentation:, :]
test_target = Y[segmentation:].reshape(-1, h)
msvr = MSVR(kernel = 'rbf', gamma = 0.1, epsilon=0.001)
# Train
msvr.fit(train_input, train_target)
# Predict with train set
trainPred = msvr.predict(train_input)
# Predict with test set
testPred = msvr.predict(test_input)
trainMetric = rmse(train_target,trainPred)
testMetric = rmse(test_target,testPred)
print(trainMetric, testMetric)This module implements sklearn.metrics.pairwise.pairwise_kernels to support multiple kernels. A brief example is given there:
msvr = MSVR(kernel = 'rbf', gamma = 0.1)
The valid metric for kernels, and the kernel functions the map to, are:
| Metric | Function |
|---|---|
| 'additive_chi2' | sklearn.pairwise.additive_chi2_kernel |
| 'chi2' | sklearn.pairwise.chi2_kernel |
| 'linear' | sklearn.pairwise.linear_kernel |
| 'poly' | sklearn.pairwise.polynomial_kernel |
| 'polynomial' | sklearn.pairwise.polynomial_kernel |
| 'rbf' | sklearn.pairwise.rbf_kernel |
| 'laplacian' | sklearn.pairwise.laplacian_kernel |
| 'sigmoid' | sklearn.pairwise.sigmoid_kernel |
| 'cosine' | sklearn.pairwise.cosine_similarity |
This program is free software: you can redistribute it and/or modify it under the terms of the Apache.
Please refer to the below papers for probably citation.
Bao, Y., Xiong, T., & Hu, Z. (2014). Multi-step-ahead time series prediction using multiple-output support vector regression. Neurocomputing, 129, 482-493.
Xiong, T., Li, C., Bao, Y., Hu, Z., & Zhang, L. (2015). A combination method for interval forecasting of agricultural commodity futures prices. Knowledge-Based Systems, 77, 92-102.
Hu, Z., Bao, Y., Chiong, R., & Xiong, T. (2015). Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection. Energy, 84, 419-431.