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

Construction of confidence interval for a univariate stock price signal predicted through Long Short Term Memory Network