Construction of confidence interval for a univariate stock price signal predicted through Long Short Term Memory Network
Machine Learning
2020-07-02 v1 Machine Learning
Statistical Finance
Abstract
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.
Keywords
Cite
@article{arxiv.2007.00254,
title = {Construction of confidence interval for a univariate stock price signal predicted through Long Short Term Memory Network},
author = {Shankhyajyoti De and Arabin Kumar Dey and Deepak Gauda},
journal= {arXiv preprint arXiv:2007.00254},
year = {2020}
}
Comments
14 pages, 11 figures