**Abstract**

Currently Dark Energy studies with type Ia supernovae rely on a spectroscopically classified sample. The Dark Energy Survey (DES) is entering its last year of observations and a first cosmological analysis with the spectroscopically confirmed supernova sample is on the way. However, present and future surveys as DES and LSST will do cosmological analysis with a photometrically classified type Ia supernova sample. For this, a reliable photometric classification is necessary, which can process large number of candidates and obtain a high-purity sample.

In this talk, I will first present preliminary cosmological parameter constraints from the first 3-years of the DES supernova survey. The sample is composed by 251 spectroscopically confirmed Type Ia Supernovae (0.02 < z < 0.9) discovered during the first 3 years of the Dark Energy Survey Supernova Program. I will also discuss about the future analysis with the DES 5-year photometric supernova sample. In particular, I will discuss a photometric classification method based on recurrent neural networks that can classify quickly large number of supernovae with high accuracy using only photometric measurements and time as input. This method includes a bayesian interpretation of classification probabilities which will be fundamental for a cosmology analysis. In addition, this method also classifies partial light-curves with high accuracy and speed which will allow to distribute resources towards promising candidates and can be applied to other transients.