I have developed a time series sales forecasting model that works better than expected. I’d like to add a variable simulating how many buyers we will have as our buyer count is predictive of our sales. I know, how many buyers are possible in each market because our buyers have to register with us in order to buy our product.
That said, is it as simple as the following:
with pm.Model(coords=coords) as constant_model: simulated_buyers = pm.TruncatedNormal('simulated_buyers', mu = "registered_buyers.mean", std = "registered_buyers.std()", upper = "registered_buyers.max") buyers_coeff = pm.Normal("buyers_coeff", mu = 0, std =1) mu= simulated_buyers*buyers_coeff sigma = pm.HalfNormal('sigma', sigma=100) eaches = pm.StudentT('predicted_eaches', mu=mu, sigma=sigma, nu=15, # lower = 0, observed=observed_eaches)
Where “registered_buyers.mean/std” are the mean and standard deviation of our registered buyers on a monthly basis?
Is there anything wrong with taking two RVs and multiplying them together?