Let's assume we have a motion sensor that provides us X and Y data for a target where the measurement error is normally distributed with parameters:
First we will need to train our prediction model with some training data. We add a square term to give the target a parabolic path.
Now build the five different supported predictor functions:
With minimal parameter input here is Information about the predictors as built with default parameters:
Now we build a new random sensor track and test it against our trained prediction functions.
Here is our "training track" plotted against the trackData1 we created.
We create a plot function that allows us to pass track data and show the first plot's prediction, confidence interval and raw data using a linear regression predictor:
We decide we'd rather parameterize the prediction method so we steel a cool method found on the Wolfram site.
Finally let's plot them all in one combined chart.
Viola' we get this chart:
The Gaussian Process provides the best fit and tightest confidence interval for our track predictor.
You can download the CDF and/or the Notebook for this work. Enjoy and let me know if you find something wrong or improve it.