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I've tried searching but couldn't find a specific answer to this question. So far I'm able to realize that Time Series Forecasting is possible using SVM. I've gone through a few papers/articles who've performed the same but didn't mention any code, instead explained the algorithm (which I didn't quite understand). And some have done it using python. My problem here is that: I have a company data(say univariate) of sales from 2010 to 2017. And I need to forecast the sales value for 2018 using SVM in R. Would you be kind enough to simply present and explain the R code to perform the same using a small example? I really do appreciate your inputs and efforts! Thanks!!!
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Support vector machines (SVMs) are promising methods for the prediction of financial time-series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to predicting the stock price index.
The results show that, besides the individual schemes, the SVM can be used to predict the data after training the learning samples, and it is necessary to use the particle swarm optimization algorithm to optimize the parameters of the support vector machine.
SVM supports both linear and non-linear solutions using kernel trick. SVM handles outliers better than LR. Both perform well when the training data is less, and there are large number of features.
Overview. Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues in 1992[5]. SVM regression is considered a nonparametric technique because it relies on kernel functions.
let's assume you have monthly data, for example derived from Air Passengers data set. You don't need the timeseries-type data, just a data frame containing time steps and values. Let's name them x and y. Next you develop an svm model, and specify the time steps you need to forecast. Use the predict function to compute the forecast for given time steps. That's it. However, support vector machine is not commonly regarded as the best method for time series forecasting, especially for long series of data. It can perform good for few observations ahead, but I wouldn't expect good results for forecasting eg. daily data for a whole next year (but it obviously depends on data). Simple R code for SVM-based forecast:
# prepare sample data in the form of data frame with cols of timesteps (x) and values (y)
data(AirPassengers)
monthly_data <- unclass(AirPassengers)
months <- 1:144
DF <- data.frame(months,monthly_data)
colnames(DF)<-c("x","y")
# train an svm model, consider further tuning parameters for lower MSE
svmodel <- svm(y ~ x,data=DF, type="eps-regression",kernel="radial",cost=10000, gamma=10)
#specify timesteps for forecast, eg for all series + 12 months ahead
nd <- 1:156
#compute forecast for all the 156 months
prognoza <- predict(svmodel, newdata=data.frame(x=nd))
#plot the results
ylim <- c(min(DF$y), max(DF$y))
xlim <- c(min(nd),max(nd))
plot(DF$y, col="blue", ylim=ylim, xlim=xlim, type="l")
par(new=TRUE)
plot(prognoza, col="red", ylim=ylim, xlim=xlim)
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