Over the last decade, the analysis of financial time series has undergone an important development, both in terms of basic research and direct market applications. In particular, the computerization of data has made detailed information on price trends and traded volumes easily available, thus creating new fields of investigation. At the same time, the introduction of electronic trading systems has made large financial institutions interested in automating trading processes through algorithms. In this thesis, the problem of predicting closing stock prices over multivariate time series using Deep Learning models was addressed. Due to the volatility and mutability of the time series, it is necessary to introduce models that allow predicting future values based on past ones, with the objective that the investor faces a lower risk of loss.