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In the convolution model $Z\_i=X\_i+ \epsilon\_i$, we give a model selectionprocedure to estimate the density of the unobserved variables $(X\_i)\_{1 \leqi \leq n}$, when the sequence $(X\_i)\_{i \geq 1}$ is strictly stationary butnot necessarily independent. This procedure depends on wether the density of$\epsilon\_i$ is super smooth or ordinary smooth. The rates of convergence ofthe penalized contrast estimators are the same as in the independent framework,and are minimax over most classes of regularity on ${\mathbb R}$. Our resultsapply to mixing sequences, but also to many other dependent sequences. When theerrors are super smooth, the condition on the dependence coefficients is theminimal condition of that type ensuring that the sequence $(X\_i)\_{i \geq 1}$is not a long-memory process.

Adaptive density deconvolution with dependent inputs