Editing Time series (section)

==Methods for analysis==
Methods for time series analysis may be divided into two classes: [[frequency-domain]] methods and [[time-domain]] methods. The former include [[frequency spectrum#Spectrum analysis|spectral analysis]] and [[wavelet analysis]]; the latter include [[auto-correlation]] and [[cross-correlation]] analysis. In the time domain, correlation and analysis can be made in a filter-like manner using [[scaled correlation]], thereby mitigating the need to operate in the frequency domain.

Additionally, time series analysis techniques may be divided into [[parametric estimation|parametric]] and [[non-parametric statistics|non-parametric]] methods. The [[parametric estimation|parametric approaches]] assume that the underlying [[stationary process|stationary stochastic process]] has a certain structure which can be described using a small number of parameters (for example, using an [[autoregressive]] or [[moving-average model]]). In these approaches, the task is to estimate the parameters of the model that describes the stochastic process. By contrast, [[non-parametric statistics|non-parametric approaches]] explicitly estimate the [[covariance]] or the [[spectrum]] of the process without assuming that the process has any particular structure.

Methods of time series analysis may also be divided into [[linear regression|linear]] and [[nonlinear regression|non-linear]], and [[Univariate analysis|univariate]] and [[multivariate analysis|multivariate]].