OBST-based segmentation approach to financial time series

Financial time series data are large in size and dynamic and non-linear in nature. Segmentation is often performed as a pre-processing step for locating technical patterns in financial time series. In this paper, we propose a segmentation method based on Turning Points (TPs). The proposed method selects TPs from the financial time series in question based on their degree of importance. A TPʹs degree of importance is calculated on the basis of its contribution to the preservation of the trends and shape of the time series. Algorithms are also devised to store the selected TPs in an Optimal Binary Search Tree (OBST) and to reconstruct the reduced sample time series. Comparison with existing approaches show that the time series reconstructed by the proposed method is able to maintain the shape of the original time series very well and preserve more trends. Our approach also ensures that the average retrieval cost is kept at a minimum.