Search results
Results From The WOW.Com Content Network
An example of statistical software for this type of decomposition is the program BV4.1 that is based on the Berlin procedure.The R statistical software also includes many packages for time series decomposition, such as seasonal, [7] stl, stlplus, [8] and bfast.
In statistics, Wold's decomposition or the Wold representation theorem (not to be confused with the Wold theorem that is the discrete-time analog of the Wiener–Khinchin theorem), named after Herman Wold, says that every covariance-stationary time series can be written as the sum of two time series, one deterministic and one stochastic.
The Berlin procedure (BV) is a mathematical procedure for time series decomposition and seasonal adjustment of monthly and quarterly economic time series. The mathematical foundations of the procedure were developed in 1960's at Technische Universität Berlin and the German Institute for Economic Research (DIW).
A working paper by Robert J. Hodrick titled "An Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data" [10] examines whether the proposed alternative approach of James D. Hamilton is actually better than the HP filter at extracting the cyclical component of several simulated time series calibrated to approximate U.S. real GDP ...
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
Download as PDF; Printable version; ... move to sidebar hide. Help. Time series is included in the JEL classification codes as JEL ... Decomposition of time series;
The Wold decomposition states that every isometry V takes the form V = ( ⨁ α ∈ A S ) ⊕ U {\displaystyle V=\left(\bigoplus _{\alpha \in A}S\right)\oplus U} for some index set A , where S is the unilateral shift on a Hilbert space H α , and U is a unitary operator (possible vacuous).
"The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis" (PDF). Proceedings of the Royal Society of London A . 454 (1971): 903– 995.