Weighted linear fit of model to data using multivariate input
3 次查看(过去 30 天)
显示 更早的评论
What is the best matlab functionality to use that allows weighted linear fit of data y using multiple predictors x, where each predictor is likely to have a different predictive power in the model, hence requiring weights, and there is a large amount of noisy data with weak correlation?
For example, for y with size 100,000 x 1 and x of size 100,000 x 3 it is possible to do this:
[b,int,r,rint,stats] = regress(y,x);
predicted = x * b;
However, this does not account for the fact that the the columns in x may require different weighting to produce optimal outcomes, eg does not produce weightings for b.
What is the best matlab function / approach for finding a weighted linear model, with the weights being for the columns (coefficients), not the datapoints?
If there is a nice tool that allows different types of low-dimensional models (eg non-linear but with only a few coefficients) to be compared as well, great!
The goal is to produce a model that makes predictions of future data with minimum least-squared error (on a validation data test set) after fitting the model on a sub-sample training set pulled from the data.
0 个评论
回答(1 个)
Benjamin Thompson
2022-2-3
Look at the features in the System Identification Toolbox.
2 个评论
Benjamin Thompson
2022-2-4
If you have a model of past system behavior you can use that to extrapolate into the future as well.
另请参阅
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!