Name
Abstract | ||
|---|---|---|
Recently, the increasing use of time series data has initiated various research and development attempts in the field of data and knowledge management. Time series data is characterized as large in data size, high dimensionality and update continuously. Moreover, the time series data is always considered as a whole instead of individual numerical fields. Indeed, a large set of time series data is from stock market. Stock time series has its own characteristics over other time series. Moreover, dimensionality reduction is an essential step before many time series analysis and mining tasks. For these reasons, research is prompted to augment existing technologies and build new representation to manage financial time series data. In this paper, financial time series is represented according to the importance of the data points. With the concept of data point importance, a tree data structure, which supports incremental updating, is proposed to represent the time series and an access method for retrieving the time series data point from the tree, which is according to their order of importance, is introduced. This technique is capable to present the time series in different levels of detail and facilitate multi-resolution dimensionality reduction of the time series data. In this paper, different data point importance evaluation methods, a new updating method and two dimensionality reduction approaches are proposed and evaluated by a series of experiments. Finally, the application of the proposed representation on mobile environment is demonstrated. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.engappai.2007.04.009 | Eng. Appl. of AI |
Keywords | Field | DocType |
time series,time series analysis,stock time series,financial time series data,data point,data size,data point importance,time series data,time series data point,financial time series,level of detail,data structure,knowledge management,tree data structure,access method,dimensionality reduction | Data point,Time series,Data mining,Dimensionality reduction,Access method,Computer science,Tree (data structure),Curse of dimensionality,Artificial intelligence,Finance,Stock market,Machine learning | Journal |
Volume | Issue | ISSN |
21 | 2 | Engineering Applications of Artificial Intelligence |
Citations | PageRank | References |
25 | 1.62 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tak-chung Fu | 1 | 407 | 21.29 |
| Fu Lai Chung | 2 | 1534 | 86.72 |
| Robert Luk | 3 | 97 | 5.88 |
| Chak-man Ng | 4 | 116 | 9.33 |