Random-Lag Singular Cross-Spectrum Analysis

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Random-Lag Singular Cross-Spectrum Analysis.

In a previous paper (Varadi et al.), random-lag singular spectrum analysis was introduced for finding oscillations in very noisy and long time series. This work presents a generalization of the technique to search for common oscillations in two or more time series.

متن کامل

Searching for Signal in Noise by Random-lag Singular Spectrum Analysis

Singular spectrum analysis, a technique to detect oscillations in short and noisy time series, was Ðrst developed for geophysical applications. This work o†ers a generalization for long and noisy time series in astrophysical applications. The motivating problem is the detection of low-amplitude solar oscillations. Subject headings : methods : data analysis È Sun: oscillations

متن کامل

Predicting the Brexit outcome using singular spectrum analysis

In a referendum conducted in the United Kingdom (UK) on June 23, 2016, $51.6\%$ of the participants voted to leave the European Union (EU). The outcome of this referendum had major policy and financial impact for both UK and EU, and was seen as a surprise because the predictions consistently indicate that the ``Remain'''' would get a majority. In this paper, we investigate whether the outcome o...

متن کامل

Nonlinear Singular Spectrum Analysis by Neural Networks

Singular spectrum analysis (SSA), a linear univariate and multivariate time series technique , is essentially principal component analysis (PCA) applied to the time series and additional copies of the time series lagged by 1 to K time steps. Neural network theory has meanwhile allowed PCA to be generalized to nonlinear PCA (NLPCA). In this paper, NLPCA is further extended to perform nonlinear S...

متن کامل

Approximate Projectors in Singular Spectrum Analysis

Singular spectrum analysis (SSA) is a method of time-series analysis based on the singular value decomposition of an associated Hankel matrix. We present an approach to SSA using an effective and numerically stable high-degree polynomial approximation of a spectral projector, which also provides a means of time-series forecasting. Several numerical examples illustrating the algorithm are given.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: The Astrophysical Journal

سال: 2000

ISSN: 0004-637X

DOI: 10.1086/312419