This paper studies the incorporation of Artificial Intelligence techniques to the set of tools available for the analysis of the regional situation. The estimates using long-short-term memory, LSTM, neural networks are compared with the most common instruments in the analysis of conjuncture (time series, synthetic indicators and dynamic factors). Results show that advances in neural networks can be incorporated into the tools used in regional economic analysis reducing the estimation error. They are complementary tools, with greater flexibility to capture the diversity of situations in the real economy and with a higher estimation capacity (lower mean square error). The document suggests the use of these types of techniques to solve a variety of problems in regional research.