  In this paper, we show an innovative way to construct bootstrap confidence
interval of a signal estimated based on a univariate LSTM model. We take three
different types of bootstrap methods for dependent set up. We prescribe some
useful suggestions to select the optimal block length while performing the
bootstrapping of the sample. We also propose a benchmark to compare the
confidence interval measured through different bootstrap strategies. We
illustrate the experimental results through some stock price data set.
