A principal component analysis (PCA) was applied to a complete annual dataset of filtered EVI2 images (based on a 12-year average over the 2001–2012 period). There was about 99% variance concentration in the first three components, with the respective loading responses and distributions (...
day. Each column of the data matrix stands for a station time series, with each element of the column represent this station solution at each day. Assuming the network time series consist of m days and n stations, the data matrix is a (m x n) matrix, usually m > n. The SVD of ...
The effect of nonstationarity in time series columns of input data in principal components analysis is examined. Nonstationarity are very common among economic indicators collected over time. They are subsequently summarized into fewer indices for purposes of monitoring. Due to the simultaneous drifting ...
Principal Components AnalysisS-modeT-modeA time series of geographic images can be viewed from two perspectives: as a set of images, each image representing a slice of time, or as a grid of temporal profiles (one at each pixel location). In the context of Principal Components Analysis (PCA...
This paper considers the analysis of cointegrated time series using principal components methods. These methods have the advantage of requiring neither the normalization imposed by the triangular error correction model nor the specification of a finite-order vector autoregression. An asymptotically efficient...
Daskin, A.: Obtaining a linear combination of the principal components of a matrix on quantum computers. Quantum Inf. Process. 15, 4013 (2016) Article ADS MathSciNet Google Scholar Brassard, G., Høyer, P., Mosca, M., Tapp, A.: Quantum Amplitude Amplification and Estimation, Contempora...
6) global principal components analysis 全局主成分分析 1. This paper intends to give the methods of principal components analysis for the instant planar data andglobal principal components analysisfor the multidimensional dynamic data system in the environmental evaluation,especially focuses on the synthesi...
Principal components analysis, which is also referred to as proper orthogonal decomposition in the literature. is a useful technique in many fields of engineering, science, and mathematics for analysis of time-series data. The benefit of principal components analysis for dynamical systems comes from ...
adeucation deucation[translate] aForecasting using principal components from a large number of predictors. Journal of the American Statistical Association 97, 1167-1179. 预测使用主要成分从很大数量的预报因子。 美国统计协会97学报日1167-1179。[translate]...
This article considers forecasting a single time series when there are many predictors (N) and time series observations (T). When the data follow an approximate factor model, the predictors can be summarized by a small number of indexes, which we estimate using principal components. Feasible fore...