# Example: Recurrent Neural Networks

Next to Dense layers, BayesFlux also implements RNN and LSTM layers. These two do require some additional care though, since the layout of the data must be adjusted. In general, the last dimension of `x`

and `y`

is always the dimension along which BayesFlux batches, which is also what Flux does. Thus, if we are in a seq-to-one setting then the sequences must be along the last dimension (here the third). To demonstrate this, let us simulate some AR1 data

BayesFlux currently only implements univariate regression problems (a single dependent variable) and for recurrent structures only seq-to-one type of settings. This can be extended by the user. For this see `BNNLikelihood`

```
Random.seed!(6150533)
gamma = 0.8
N = 500
burnin = 1000
y = zeros(N + burnin + 1)
for t=2:(N+burnin+1)
y[t] = gamma*y[t-1] + randn()
end
y = Float32.(y[end-N+1:end])
```

Just like in the FNN case, we need a network structure and its constructor, a prior on the network parameters, a likelihood with a prior on the additional parameters introduced by the likelihood, and an initialiser. Note how most things are the same as for the FNN case, with the differences being the actual network defined and the likelihood.

```
net = Chain(RNN(1, 1), Dense(1, 1)) # last layer is linear output layer
nc = destruct(net)
like = SeqToOneNormal(nc, Gamma(2.0, 0.5))
prior = GaussianPrior(nc, 0.5f0)
init = InitialiseAllSame(Normal(0.0f0, 0.5f0), like, prior)
```

We are given a single sequence (time series). To exploit batching and to not always have to feed through the whole sequence, we will split the single sequence into overlapping subsequences of length 5 and store these in a tensor. Note that we add 1 to the subsequence length, because the last observation of each subsequence will be our training observation to predict using the fist five items in the subsequence.

```
x = make_rnn_tensor(reshape(y, :, 1), 5 + 1)
y = vec(x[end, :, :])
x = x[1:end-1, :, :]
```

We are now ready to create the BNN and find the MAP estimate. The MAP will be used to check whether the overall network structure makes sense (does provide at least good point estimates).

```
bnn = BNN(x, y, like, prior, init)
opt = FluxModeFinder(bnn, Flux.RMSProp())
θmap = find_mode(bnn, 10, 1000, opt)
```

When checking the performance we need to make sure to feed the sequences through the network observation by observation:

```
nethat = nc(θmap)
yhat = vec([nethat(xx) for xx in eachslice(x; dims =1 )][end])
sqrt(mean(abs2, y .- yhat))
```

The rest works just like before with some minor adjustments to the helper functions.

```
sampler = SGNHTS(1f-2, 1f0; xi = 1f0^2, μ = 10f0)
ch = mcmc(bnn, 10, 50_000, sampler)
ch = ch[:, end-20_000+1:end]
chain = Chains(ch')
function naive_prediction_recurrent(bnn, draws::Array{T, 2}; x = bnn.x, y = bnn.y) where {T}
yhats = Array{T, 2}(undef, length(y), size(draws, 2))
Threads.@threads for i=1:size(draws, 2)
net = bnn.like.nc(draws[:, i])
yh = vec([net(xx) for xx in eachslice(x; dims = 1)][end])
yhats[:,i] = yh
end
return yhats
end
```

```
yhats = naive_prediction_recurrent(bnn, ch)
chain_yhat = Chains(yhats')
maximum(summarystats(chain_yhat)[:, :rhat])
```

```
posterior_yhat = sample_posterior_predict(bnn, ch)
t_q = 0.05:0.05:0.95
o_q = get_observed_quantiles(y, posterior_yhat, t_q)
plot(t_q, o_q, label = "Posterior Predictive", legend=:topleft,
xlab = "Target Quantile", ylab = "Observed Quantile")
plot!(x->x, t_q, label = "Target")
```