Cyclic Nonlinear Correlation Analysis for Time Series
نویسندگان
چکیده
Principal component analysis (PCA) and kernel PCA allow the decorrelation of data with respect to a basis that is found via variance maximization. However, these techniques are based on pointwise correlations. Especially in context time series this not optimal. We present novel generalization allows imprint any desired correlation pattern. Thus proposed method can be used incorporate previously known statistical dependencies between input variables into model which increasing overall performance. This achieved by generalizing projection onto direction maximum variance—as from PCA—to multi-dimensional subspace. focus use cyclic patterns, especially interest domain analysis. Beneath introducing presented variation PCA, we discuss role other well-known techniques.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3218163