Fitting a Stochastic Volatility model to my data

Questions
1

Hi
I have a dataset containing arrival counts aggregated for 15min interval. The rate is changing fluctuating across the day and repeats across several days. Below is a snapshot over several days

data_visual
data_visual927×299 56.2 KB

I am trying to a fit a Stochastic Volatility model based on the example in the docs:
https://docs.pymc.io/notebooks/stochastic_volatility.html. Below is the code i am using

   with pm.Model() as model:
        step_size = pm.Exponential('step_size', 10)
        volatility = pm.GaussianRandomWalk('volatility', sigma=step_size, shape=len(data))
        nu = pm.Exponential('nu', 0.1)
        returns = pm.StudentT('arrivals',
                        nu=nu,
                        lam=np.exp(-2*volatility),
                        observed=data)

When i sample from the model, i get below message:

Sampling 2 chains, 4,000 divergences: 100%|██████████| 14000/14000 [01:36<00:00, 145.05draws/s]
The chain contains only diverging samples. The model is probably misspecified.
The chain contains only diverging samples. The model is probably misspecified.
The acceptance probability does not match the target. It is 0.8917233554736689, but should be close to 0.8. Try to increase the number of tuning steps.
The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.
The estimated number of effective samples is smaller than 200 for some parameters.

Any suggestions on how to adapt to my dataset ?

Thanks