Estimate a spectral density function of a stationary time series. Produces a linear Bayes estimate with credible intervals. Can incorporate time series data from multiple realizations with different sampling rate. Can deal with time series data that has been filtered with a known filter (e.g. quarterly totals from monthly values).
Currently, the package's centrepiece is the function
regspec, which implements spectral density estimation from time series data at integer time points.
A novel element of the code is it's ability to assimilate subsampled and filter data.
The package's data files, which will be loaded automatically, include synthetic and real examples of time series data that feature in the
Examples sections of the functions' help files.
Nason, G.P., Powell, B., Elliott, D. and Smith, P. (2016) Should We Sample a Time Series More Frequently? Decision Support via Multirate Spectrum Estimation. Journal of the Royal Statistical Society, Series A., 179, (to appear).
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