Prediction techniques have the challenge of guaranteeing large horizons for chaotic time series. For instance, this paper shows that the majority of techniques can predict one step ahead with relatively low root-mean-square error (RMSE). However, some techniques based on neural networks can predict more steps with similar RMSE values. In this manner, this work provides a summary of prediction techniques, including the type of chaotic time series, predicted steps ahead, and the prediction error. Among those techniques, the echo state network (ESN), long short-term memory, artificial neural network and convolutional neural network are compared with similar conditions to predict up to ten steps ahead of Lorenz-chaotic time series. The comparison among these prediction techniques include RMSE values, training and testing times, and required memory in each case. Finally, considering RMSE, with relatively few neurons in the reservoir, the performance comparison shows that an ESN is a good technique to predict ten steps ahead using thirty neurons and taking the lowest time for the tracking and testing cases.