Segmentation and Hashing of Time Series in Stock Market Prediction

Abstract

In this paper, we associate each time series of a stock price (TS-P) in a stock market with a time series of hash codes (TS-HC) that indicate price increase or decrease for each element of the TS-P. As noted, in this case hash codes represent integer numbers and their sequence allows to identify the same (typical) groups of TS-P elements in the stock price dynamics. We describe the procedures for transforming an initial time series and calculating the hash codes. The main properties of a sequence of hash codes are established. Finally, we suggest an analysis and prediction method for a stock price trajectory using segmentation and hashing data.