We present a neural network technique for the analysis and extrapolation of time-series data
called neural decomposition (ND). Units with a sinusoidal activation function are used to …

… Decomposition of time-series by level and change … This article examines the additive
decomposition of time-series data by level and change. Little comparative research has been done …

… The decomposition of time series data into trend and cycle is very important in making
this distinction and great care should be taken to estimate trends properly when performing …

In exploration seismology, a charge of dynamite is exploded under controlled conditions,
and the resulting vibrations at various points on the surface of the ground are detected by …

A technique for scalar and multidimensional spectral analysis based on the autoregressive
representation of the observed data records is presented and illustrated. In an autoregressive …

This paper derives the admissible decompositions for a time series dynamic linear model,
assuming only that the model is observable. The decompositions depend on factorizations of …

… A proof was the basis for the development of EMD-RP and EMD-MI, two new approaches
for the decomposition of time series into stochastic and deterministic components. …

With the rapid development and evolution of sophisticated algorithms for statistical analysis
of time series data, the research community has started spending considerable effort in …

… In this section, we make use the decomposition of time-series to mine periodic patterns. The
… However, our approach using decomposition of time series and partitioning of time series …

… In general, the main objective of this research is to find techniques related to the
decomposition of time series in terms of stochastic and deterministic components. Based on this …