time series cross validation in svm

3 次查看（过去 30 天）
mono
mono 2019-8-7
评论： mono 2023-5-3
HI,
I am trying to write a kernel based regression model (svm or gaussian process) to predict time series data. I note that fitrsvm has cross validation input arguement that random shuffs the set and generate both training and validation sets. BUT, I am working on a time series data that the built in cross validation or kfold methods is not a good choice due to the data sequencial property.
Would be appreciated if anyone can give a tip or two to tackle the task while still using fitrgp or fitrsvm functions...

请先登录，再回答此问题。

回答（1 个）

Drew
Drew 2023-1-20
In R2022b, the "tspartition" function for time series partition was introduced: https://www.mathworks.com/help/stats/tspartition.html
An example of the use of tspartition is here: https://www.mathworks.com/help/stats/time-series-forecasting-using-ensemble-of-boosted-regression-trees.html . A similar workflow would apply to fitrgp or fitrsvm.
In earlier releases of MATLAB, before tspartition was introduced, you always have the option to create your own partition and then control the partitioning for validation experiments by directly passing datasets to the relevant model-buillding and model-testing functions.
  1 个评论
mono
mono 2023-5-3
Thanks for the information. I checked out the sample using tspartition. The basic idea is to seperate the whole data set into "old" set and "future" set, and use "old" one for training and "future" one for test.
singleHoldoutModel = fitrensemble(X(trainIdx,:),y{trainIdx,:}, ...
Method="LSBoost",LearnRate=0.2,NumLearningCycles=150, ...
Learners=tree,CategoricalPredictors=catPredictors);
However, take fitrenseble as an example, if we want to optimize hyperparameters, the whole data set would be used directly within the fitrensemble command, e.g.:
Mdl = fitrensemble([Horsepower,Weight],MPG,'OptimizeHyperparameters','auto')
I think I am stuck at how to use fitrensemble to optimize hyperparamters while still take the data sequential property into consideration.

请先登录，再进行评论。

类别

在 Help Center 和 File Exchange 中查找有关 Support Vector Machine Regression 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by