| Full text | |
| Author(s): |
Duarte, Felipe S. L. G.
[1]
;
Rios, Ricardo A.
[2]
;
Hruschka, Eduardo R.
[3, 4]
;
de Mello, Rodrigo F.
[1]
Total Authors: 4
|
| Affiliation: | [1] Univ Sao Paulo, ICMC, Sao Carlos, SP - Brazil
[2] Univ Fed Bahia, DCC, Salvador, BA - Brazil
[3] Univ Sao Paulo, Comp Engn & Digital Syst Dept, Sao Paulo, SP - Brazil
[4] Itau Unibanco, Data Sci Team, Sao Paulo, SP - Brazil
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | DIGITAL SIGNAL PROCESSING; v. 95, DEC 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
Temporal data produced by industrial, human, and natural phenomena typically contain deterministic and stochastic influences, being the first ideally modelled using Dynamical Systems while the second is appropriately addressed using Statistical tools. Although such influences have been widely studied as individual components, specific tools are required to support their decomposition for a proper modeling and analysis. This article addresses a comprehensive survey of the main time-series decomposition strategies and their relative performances in different application domains. The following strategies are discussed: i) Fourier Transform, ii) Wavelet transforms, iii) Moving Average, iv) Singular Spectrum Analysis, v) Lazy, vi) GHKSS, and vii) other approaches based on the Empirical Mode Decomposition method. In order to assess these strategies, we employ diverse and complementary performance measures: i) Mean Absolute Error, Mean Squared and Root Mean Squared Errors; ii) Minkowski Distances; iii) Complexity-Invariant Distance; iv) Pearson correlation; v) Mean Distance from the Diagonal Line; and vi) Mean Distance from Attractors. Each decomposition strategy is better devoted to particular scenarios, however, without any previous knowledge on data, GHKSS confirmed to work as a fair and general baseline besides its time complexity. (C) 2019 Elsevier Inc. All rights reserved. (AU) | |
| FAPESP's process: | 17/16548-6 - Providing theoretical guarantees to the detection of concept drift in data streams |
| Grantee: | Rodrigo Fernandes de Mello |
| Support Opportunities: | Scholarships abroad - Research |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 14/21636-3 - Time series decomposition preserving deterministic influences |
| Grantee: | Felipe Simões Lage Gomes Duarte |
| Support Opportunities: | Scholarships in Brazil - Doctorate |