کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6951771 1451703 2018 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improving reconstruction of time-series based in Singular Spectrum Analysis: A segmentation approach
ترجمه فارسی عنوان
بهبود بازسازی سری زمانی بر اساس تجزیه و تحلیل طیف منحصر به فرد: رویکرد تقسیم بندی
کلمات کلیدی
تجزیه و تحلیل طیف منحصر به فرد، سیگنال های غیر ثابت، تقسیم بندی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی
Singular Spectrum Analysis (SSA) is a powerful non-parametric framework to analysis and enhancement of time-series. SSA may be capable of decomposing a time-series into its meaningful components: trends, oscillations and noise. However, if the signal under analysis is non-stationary, with its spectrum spreading and varying in time, the reliability of the reconstruction is guaranteed only when many elementary matrices are used. As a consequence, the capability to discriminate dominant structures from time-series may be impaired. To circumvent this issue, a new method, called overlap-SSA (ov-SSA), is proposed for segmentation, analysis and reconstruction of long-term and/or non-stationary signals. The raw time series is divided into smaller, consecutive and overlapping segments, and standard SSA procedures are applied to each segment with the resulting series being concatenated. This variation of SSA seeks to: improve reconstruction and component separability for non-stationary time-series; enable the analysis for large datasets, avoiding the issues of concatenation of many segments; and present some benefits of the segmentation in terms of better time-frequency characterization. These advantages are illustrated in several synthetic and experimental datasets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Digital Signal Processing - Volume 77, June 2018, Pages 63-76
نویسندگان
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