The mean \(\mu_t\) of such distribution is modeled using three components: seasonality (\(\lambda_t\)), an autoregressive term on the latent mean (\(\mu_{t - 1}\)) and an autoregressive sales model. The seasonality component includes a linear trend, in-week seasonality via day of week indicator functions and long term seasonality modeled using Fourier modes. The key point to note is that the prior of such Fourier modes are actually determined by the posterior distribution obtained from the temperature model. Now we write the model above in PyMC. As always, is always good to run prior predictive checks before fitting the model.