scikit learn - Python Gaussian Process Regression with sklearn

Question

I'm attempting to do a regression to fit a function to some data points I have, these are simply put (x,y) where x = date and y = a point of data. Seems simple enough.

I'm following along on a how-to and it comes to the part where you split your data into training/testing, that much I understand, but the input for model.fit is a 2D array and then labels.

I think I'm being incredibly dense, but this is what I have for that:

model.fit(input, date_time_training)

My input is an array like so [[5, 3], [7,5], etc] my "labels" are dates because that's how I'd want to label my data but that's not right, they need to be numbers. There are two things it could be, though, my data points which are y on my graph and my x-axis which are dates. I converted my dates into numbers (0,1,2,3,etc) corresponding to each date.

Is that also what my labels would be?

Also my input is just [[date_converted_to_int, score], etc] which when looking at the documentation, seemingly that should be [[points, features], etc]. I'm pretty confused, obviously not super experienced with regression either (otherwise I'm guessing this would be clearer).

question from:https://stackoverflow.com/questions/65930330/python-gaussian-process-regression-with-sklearn

Answer 1

You are trying to predict {actual term is forecast in this case} your y over time. So, It is more suitable to use a time series model in this case. Because by definition this is a time series use case. [time series: you try to understand the evolution of values of an attribute over time] Try some models like:

• AR
• ARIMA
  and
  statsmodel would be a nice place to visit by for documentation