Statistics and Its Interface

Volume 16 (2023)

Number 2

Special issue on recent developments in complex time series analysis – Part II

Guest editors: Robert T. Krafty (Emory Univ.), Guodong Li (Univ. of Hong Kong), Anatoly Zhigljavsky (Cardiff Univ.)

Quantile recurrent forecasting in singular spectrum analysis for stock price monitoring

Pages: 189 – 197



Atikur R. Khan (North South University, Dhaka, Bangladesh)

Hossein Hassani (Research Institute of Energy Management and Planning, University of Tehran, Iran)


Monitoring of near real-time price movement is necessary for data-driven decision making in opening and closing positions for day traders and scalpers. This can be done effectively by constructing a movement path based on forecast distribution of stock prices. High frequency trading data are generally noisy, nonlinear and nonstationary in nature. We develop a quantile recurrent forecasting algorithm via the recurrent algorithm of singular spectrum analysis that can be implemented for any type of time series data. When applied to median forecasting of deterministic and shortand long-memory processes, our quantile recurrent forecast overlaps the true signal. By estimating only the signal dimension number of parameters, this method can construct a recurrent formula by including many lag periods. We apply this method to obtain median forecasts for Facebook, Microsoft, and SNAP’s intraday and daily closing prices. Both for intraday and daily closing prices, the quantile recurrent forecasts produce lower mean absolute deviation from original prices compared to bootstrap median forecasts. We also demonstrate the tracing of price movement over forecast distribution that can be used to monitor stock prices for trading strategy development.


Forecast distribution, recurrent forecasting, quantile, stock price, trading

2010 Mathematics Subject Classification

37M10, 91B84

Received 23 May 2021

Accepted 24 December 2021

Published 13 April 2023