I'm an undergrad interested in machine learning, and I'm playing around with some data in order to get a better understanding of the field.
I'm working with the following data:
To give you an idea of what you're looking at (sorry for the unlabeled axes) -- each data point corresponds to a basketball player's points per game (y-values) over a one-week period (x-values).
I want to predict progress throughout the year, so in order to test/train, I start with the first two points, fit a regression model, and then predict the third point. I then add the actual value of the third point to the set, re-train, and predict the fourth point, etc.
As you can see, the data is all over the place, and the predictions are just as messy (sometimes getting up to ~200 points per game, which is totally impossible).
I've tested different degrees of linear regression (quadratic, cubic, etc.) and degree=1 is always the best predictor because of how wonky the data is.
I have thought of the following ways to get more accurate predictions:
- Smoothing the data, maybe using a moving average or some variant
- Set an upper limit for predictions
- Non-linear regression
But outside of smoothing the data, I'm not sure if the rest are even possible in a regression model (upper limit) or applicable to this situation (non-linear).
Are any of the ideas I had above worth pursuing? If not, is there anything I should look into that might help me solve this problem?