Analysis of Stock Price Data: Determinition of The Optimal Sliding-Window Length - Liu, Xuebin; Yuan, Xuesong; Liu, Chang; Ma, Hao; Lian, Chongyang (Frontiers in Physics, 2021)
Over the recent years, the study of time series visualization has attracted great interests. Numerous scholars spare their great efforts to analyze the time series using complex network technology with the intention to carry out information mining. While Visibility Graph and corresponding spin-off technologies are widely adopted. In this paper, we try to apply a couple of models derived from basic VG to construct complex networks on one-dimension or multi-dimension stock price time series. As indicated by the results of intensive simulation, we can predict the optimum window length for certain time series for the network construction. This optimum window length is long enough to the majority of stock price SVG whose data length is one-year. The optimum length is 70% of the length of stock price data series.