Developing a Custom Attention Mechanism for Multivariate Time Series Forecasting This thesis work focuses on the development of a new attention mechanism to tailor the Transformer architecture to multivariate time series. In particular, the performance of two attention mechanism were compared. The first is the classical attention mechanism commonly used in ’vanilla’ transformers. The second, that we will refer to a ’cross-attention’, is a modification aimed to improve the projection representation in the attention to learn the most critical features for multivariate time series forecasting. Therefore, three mains use case projections Embedding were introduced. The first tested the influence of the embedding dimension in capturing the long terms dependence and long time, however it could have a better reactivities in the early event.