2016/03/16 by Mostafa Rostaghi, Hamed Azami · 2 citations
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Chaos control and synchronization #Complex Systems and Time Series Analysis #Time Series Analysis and Forecasting
paper · doi:10.1109/lsp.2016.2542881
openalex publication_date 2016/03/16 · crossref created 2016/03/16 · crossref issued 2016/05/01 · crossref published 2016/05/01 · crossref published-print 2016/05/01 · crossref deposited 2022/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29 · crossref indexed 2026/07/30
One of the most powerful tools to assess the dynamical characteristics of time series is entropy. Sample entropy (SE), though powerful, is not fast enough, especially for long signals. Permutation entropy (PE), as a broadly used irregularity indicator, considers only the order of the amplitude values and hence some information regarding the amplitudes may be discarded. To tackle these problems, we introduce a new method, termed dispersion entropy (DE), to quantify the regularity of time series. We gain insight into the dependency of DE on several straightforward signal-processing concepts via a set of synthetic time series. The results show that DE, unlike PE, can detect the noise bandwidth and simultaneous frequency and amplitude change. We also employ DE to three publicly available real datasets. The simulations on real-valued signals show that the DE method considerably outperforms PE to discriminate different groups of each dataset. In addition, the computation time of DE is significantly less than that of SE and PE.