tspredit (version 1.2.767)

ts_svm: SVM

Description

Create a time series prediction object that uses Support Vector Regression (SVR) on sliding-window inputs.

It wraps the e1071 package to fit epsilon-insensitive regression with linear, radial, polynomial, or sigmoid kernels.

Usage

ts_svm(
  preprocess = NA,
  input_size = NA,
  kernel = "radial",
  epsilon = 0,
  cost = 10
)

Value

A ts_svm object (S3) inheriting from ts_regsw.

Arguments

preprocess

Normalization preprocessor (e.g., ts_norm_gminmax()).

input_size

Integer. Number of lagged inputs used by the model.

kernel

Character. One of 'linear', 'radial', 'polynomial', 'sigmoid'.

epsilon

Numeric. Epsilon-insensitive loss width.

cost

Numeric. Regularization parameter controlling margin violations.

Details

SVR aims to find a function with at most epsilon deviation from each training point while being as flat as possible. The cost parameter controls the trade-off between margin width and violations; epsilon controls the insensitivity tube width. RBF kernels often work well for nonlinear series; tune cost, epsilon, and kernel hyperparameters.

References

• C. Cortes and V. Vapnik (1995). Support-Vector Networks. Machine Learning, 20, 273–297.

Examples

# Example: SVR with min–max normalization
# Load package and dataset
library(daltoolbox)
data(tsd)

# Create sliding windows and preview
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)

# Temporal split and (X, y) projection
samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Define SVM regressor and fit to training data
model <- ts_svm(ts_norm_gminmax(), input_size = 4)
model <- fit(model, x = io_train$input, y = io_train$output)

# Multi-step forecast and evaluation
prediction <- predict(model, x = io_test$input[1,], steps_ahead = 5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

ev_test <- evaluate(model, output, prediction)
ev_test