dekompositionsmodeller
Dekompositionsmodeller refer to a class of statistical and data-analytic models whose primary purpose is to decompose a complex data set into interpretable constituent components. The term, derived from the concept of decomposition in statistics, covers a range of modeling approaches across disciplines such as time series analysis, signal processing, chemometrics and image analysis. In general, these models seek to represent data as a sum or product of simpler factors that capture underlying structures such as trend, periodic variation, and residual noise, or as low-rank factors that explain shared variation across variables.
Two common forms are additive and multiplicative decompositions. Additive models express the observation as a sum
Typical methods include singular value decomposition, principal component analysis, independent component analysis, nonnegative matrix/tensor factorization, and
Applications span finance, climatology, energy forecasting, biomedical signal processing, and image or audio analysis. Ongoing developments
See also: time series decomposition, matrix factorization, tensor decomposition, and related factor-analysis methods.