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Compression Methods for Time Series Data using Discrete Cosine Transform with Varying Sample Size
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 Title & Authors
Compression Methods for Time Series Data using Discrete Cosine Transform with Varying Sample Size
Moon, Byeongsun; Choi, Myungwhan;
 
 Abstract
Collection and storing of multiple time series data in real time requires large memory space. To solve this problem, the usage of varying sample size is proposed in the compression scheme using discrete cosine transform technique. Time series data set has characteristics such that a higher compression ratio can be achieved with smaller amount of value changes and lower frequency of the value changes. The coefficient of variation and the variability of the differences between adjacent data elements (VDAD) are presumed to be very good measures to represent the characteristics of the time series data and used as key parameters to determine the varying sample size. Test results showed that both VDAD-based and the coefficient of variation-based scheme generate excellent compression ratios. However, the former scheme uses much simpler sample size decision mechanism and results in better compression performance than the latter scheme.
 Keywords
discrete cosine transform;compression;time series data;coefficient of variation;variability;
 Language
Korean
 Cited by
1.
유사 시계열 데이터 분석에 기반을 둔 교육기관의 전력 사용량 예측 기법,문지훈;박진웅;한상훈;황인준;

정보과학회 논문지, 2017. vol.44. 9, pp.954-965 crossref(new window)
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