Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network
This work addresses uncertainty quantification in financial time series forecasting, but appears incremental as it builds on existing bootstrap methods.
The paper tackles the problem of constructing confidence intervals for multivariate time series predictions using LSTM networks, proposing novel block bootstrap techniques and a block length selection procedure, and demonstrates the approach on S&P 500 and Dow Jones Index datasets.
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.