A multiresolution important point retrieval method for financial time series representation

Mining financial time series data without ignoring its characteristics is very important. Financial time series data normally fluctuate unexpectedly which courses very high dimensions. The peak and the dip points of the series may appear frequently over time. These points are known as the most important points which reflect some related events to the market. However, to manipulate financial time series, researchers usually decrease this complexity of time series in their techniques. Consequently, transforming the time series into another easily understanding representation is usually considered as an appropriate approach. In this paper, we propose a multiresolution important point retrieval method for financial time series representation. The idea of the method is based on finding the most important points in multiresolution. These retrieved important points are recorded in each resolution. The collected important points are used to construct the TS-binary search tree. From the TS-binary search tree, the application of time series segmentation is conducted. The experimental results show that the TS-binary search tree representation for financial time series exhibits different performance in different number of cutting points, however, in the empirical results, the number of cutting points which are larger than 12 points show the better results.