To tackle the chaos time series interval prediction problem with high risk, this paper proposes an algorithm combining the ideas of echo state network and conformal prediction. While inheriting the learning ability from echo state network, the algorithm is capable of predicting reliable intervals for chaos time series with the help of the learning framework of conformal prediction, as the error rate of the prediction intervals can be controlled by the preset parameter named significance level. Meanuhice, due to the use of ridge regression to train the output weights of echo state network, the leave-one-out estimates in the training set can be computed efficiently, which accelerates the learning process of the algorithm effectively. As such, from the point of view of algorithm complexity, it is shown that the time complexity of the whole algorithm is equivalent to that of echo state network, implying that the algorithm is as fast and of practical use as echo state network. The experimental results show that the proposed algorithm is empirically valid as an interval predictor, robust to noise, and the prediction intervals output by the proposed algorithm are more related to the variance of the predicted values than those output by Gaussian process.