  In this paper we propose a novel procedure to construct a confidence interval
for multivariate time series predictions using long short term memory network.
The construction uses a few novel block bootstrap techniques. We also propose
an innovative block length selection procedure for each of these schemes. Two
novel benchmarks help us to compare the construction of this confidence
intervals by different bootstrap techniques. We illustrate the whole
construction through S\&P $500$ and Dow Jones Index datasets.

Methodology;Machine Learning;Computational Finance;Computation