Forecasting the dynamics of chaotic systems from the analysis of their output signals is a challenging problem with applications in most fields of modern science. In this work, we use a laser model to compare the performance of several machine learning algorithms for forecasting the amplitude of upcoming emitted chaotic pulses. We simulate the dynamics of an optically injected semiconductor laser that presents a rich variety of dynamical regimes when changing the parameters. We focus on a particular regime where the intensity shows a chaotic pulsing dynamics, and occasionally an ultra-high pulse, reminiscent of a rogue wave, is emitted. Our goal is to predict the amplitude (height) of the next pulse, knowing the amplitude of the three preceding pulses. We compare the performance of several machine learning methods, namely neural networks, support vector machine, nearest neighbors and reservoir computing. We analyze how their performance depends on the length of the time-series used for training.P. A. acknowledge support of the Marie Sklodowska-Curie Innovative Training Network Advanced BiomEdical OPTICAL Imaging and Data Analysis (BE-OPTICAL, H2020-675512, http://beoptical.eu). C. M. acknowledges support from the Spanish Ministerio de Ciencia, Innovaci贸n y Universidades (PGC2018-099443-B-I00) and ICREA ACADEMIA. M. C. S. was supported through a \Ramon y Cajal" Fellowship (RYC-2015-18140). This work is the result of a collaboration established within the Ibersinc network of excellence (FIS2017-90782-REDT)Peer ReviewedPostprint (published version
