| Author: | Ng, Yiu Wai |
| Title: | Similarity search for time series subsequence under dynamic time warping |
| Advisors: | Yiu, Ken (COMP) |
| Degree: | M.Sc. |
| Year: | 2020 |
| Subject: | Time-series analysis -- Data processing Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Computing |
| Pages: | vi, 40 pages : color illustrations |
| Language: | English |
| Abstract: | Time series data can be pervasively found in many fields. We are always interested in retrieving knowledge from existing data. Finding similar subsequences could be the first step before preforming any analyses. Dynamic time warping is one of the best distance functions to measure similarity between two subsequences. However, the time complexity for computation of dynamic time warping is O(N2) for a query of length N, which is slow to calculate. To accelerate the computation, one method is to use lower bound functions. LB_Keogh and its variants are the most popular lower bound functions, which have time complexity of O(N). In this dissertation, we will focus on exact sequential search on normalized time series sequences under dynamic time warping for large dataset, with the assumption that dataset is non-segmented and lower bound function LB_Keogh is used. The state-of-art method is UCR Suite. The contribution of this dissertation is to improve the efficiency of UCR Suite under the scenario that (i) we are interested in finding similar subsequences for a few arbitrary-length queries, but not a single query, and (ii) the lengths of queries are long. A new lower bound function, namely LB_LowResED, is introduced. It is a lower bound function of LB_Keogh. The two assumptions are usually true for financial data analysis. Users would like to ask a few arbitrary-length long queries on the same set of financial data. This dissertation is composed of four parts: (i) introduction of time series sequences searching, (ii) related works, especially for the state-of-art method UCR Suite, (iii) introduction of the new lower bound function LB_LowResED, and (iv) experiment results. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5809.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.5 MB | Adobe PDF | View/Open |
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