Understanding how DPPy is used in the documentation and how to apply it to real data

I am trying to use Determinantal Point Processes (DPPs) to 'model' the distributions of my data, in particular the 'execution time' of a task. To make it clearer, I have a dataset with the columns order_id and wait_time (which I cannot share due to confidentiality reasons), which records the time a customer had to wait for in seconds for their order.

To date, I've only managed to obtain a best-fitted distribution using in-house sklearn distributions, with reference to How to find probability distribution and parameters for real data? (Python 3). I recently chanced upon the article https://arxiv.org/abs/1809.07258 with an implementation in Python.

I'm still trying to make sense of parts of the documentation, say https://dppy.readthedocs.io/en/latest/finite_dpps/definition.html and https://dppy.readthedocs.io/en/latest/finite_dpps/exact_sampling.html#finite-dpps-exact-sampling-k-dpps. My objective is to obtain simulated samples of the data for the following year (e.g. 2015), using the current dataset above for year 2014 for e.g.

Is it possible to utilize dppy to obtain these simulated samples with my current dataset? I'm currently looking at the following code in the documentation to obtain the samples:

import numpy as np
from dppy.finite_dpps import FiniteDPP

rng = np.random.RandomState(1)

r, N = 5, 10
# Random feature vectors
Phi = rng.randn(r, N)
DPP = FiniteDPP('likelihood', **{'L': Phi.T.dot(Phi)})

k = 4
for _ in range(10):
    DPP.sample_exact_k_dpp(size=k, random_state=rng)

print(list(map(list, DPP.list_of_samples)))

Am I sort of on the right track, or do I simply have a big misconception overall on the applications of dppy for my case?

I understand that this site may not be the right one for this question, but it'll be great if I can get some sort of direction and/or recommendation as to the best of my findings and understanding, DPPs have not (possibly have, but am unable to find examples) been implemented for use cases such as mine, in Python.