Name
Title | ||
|---|---|---|
A piecewise linear representation method based on importance data points for time series data |
Abstract | ||
|---|---|---|
With the development of intelligent manufacturing technology, it can be foreseen that time series data generated by smart devices will raise to an unprecedented level. For time series with high amount, high dimension and renewal speed characteristics, resulting in difficult data mining and presentation on the original time series data. This paper presented a piecewise linear representation based on importance data points for time series data, which called PLR_IDP for short. The method finds importance data points by calculating the fitting error of single point and piecewise, and then represents time series approximately by linear composed of the importance data points. Results from theoretical analysis and experiments show that PLR_IDP reduces the dimensionality, holds the main characteristic with small fitting error of segments and single points. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CSCWD.2016.7565973 | 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) |
Keywords | Field | DocType |
time series data,importance data point,piecewise linear representation,fitting error | Data point,Time series,Data mining,Manufacturing technology,Algorithm design,Computer science,Curse of dimensionality,Big data,Piecewise linear representation,Piecewise | Conference |
ISBN | Citations | PageRank |
978-1-5090-1916-8 | 4 | 0.42 |
References | Authors | |
12 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cun Ji | 1 | 11 | 2.06 |
| Shijun Liu | 2 | 31 | 16.68 |
| Chenglei Yang | 3 | 219 | 35.20 |
| Lei Wu | 4 | 73 | 17.47 |
| Li Pan | 5 | 39 | 18.95 |
| Xiangxu Meng | 6 | 308 | 60.76 |