Please forgive me if my question does not make sense - I am pretty new to stats and could use some guidance.
I would like to predict the next position of an object in 3D space. I have a list for each axis: xData, yData, zData - these lists contain the coordinates of the object. For example:
# | xData | yData | zData |
- | --------------------- |
1 | 1.245 | 2.452 | 5.125 |
2 | 3.256 | 4.357 | 7.425 |
n | ..... | ..... | ..... |
So, the first set of coordinates would be 1: {1.245, 2.452, 5.125}
I have thought about using a Kalman filter, but I am not sure what to do for the "predict" stage when following this tutorial.
A friend recommended building a multivariate dynamic linear model... but I don't know where to start.
Questions:
- Would the Kalman filter work for this? (predicting the next set of coordinates xyz)
- Would the multivariate dynamic linear model work better? If so, is it quicker to compute the next set of coordinates than the Kalman filter? (I need to keep computation times as low as possible)
- Any recommended tutorials would be appreciated