# Model Selection (Count Data, Time Series)

So I'm in a bit of a rut here... I've been trying to fit a dynamic linear model with my data to be able to gather some information and I can't seem to get what I wan't.

What I'm trying to achieve: I want to observe how the coefficients of my predictors change over time.

The Data: My response is essentially count data, the # of animals that have used a specific bridge. I have in my data 10 bridges, each with their own qualities (which I use as regressors).

The count is collected at each bridge once every month for 18 years. So in total I have 2160 data points.

Now there is definitely seasonality in my Data, I.E I expect to have higher counts during specific months. So I have to take this into consideration in my model.

Since the bridges are located nearby, theres the issue of spatial correlation, which I tackle using the 'autocovariate' method. Essentially creating a new regressor to apply a 'weight' to each bridge depending on how many other bridges are nearby at a certain radius.

The end goal: Making sure the model is a good fit, via checking MSE, I want to be able to plot the coefficient values of certain predictors in the model over time.

Any specific models that anyone would recommend? (Along with their package details on R if possible)

Your data set is $X_{10\times 216}$. The first thing I would do is SVD. For background material, see