Time-series Imputation Algorithm.pdf (880.56 kB)
Time-series Imputation Algorithm
Statistical imputation is a field of study that
attempts to fill missing data. It is commonly applied to population statistics
whose data have no correlation with running time. For a time series, data is
typically analyzed using the autocorrelation function (ACF), the Fourier
transform to estimate power spectral densities (PSD), the Allan deviation
(ADEV), trend extensions, and basically any analysis that depends on uniform
time indexes. We explain the rationale
for an imputation algorithm that fills gaps in a time series by applying a
backward, inverted replica of adjacent live data. To illustrate, four intentional massive gaps that
exceed 100% of the original time series are recovered. The L(f)
PSD with imputation applied to the gaps is nearly indistinguishable from the
original. Also, the confidence of ADEV with imputation falls within 90% of the
original ADEV with mixtures of power-law noises. The algorithm in Python is
included for those wishing to try it.
History
Email Address of Submitting Author
david.howe@nist.govORCID of Submitting Author
https://orcid.org/0000-0002-1991-8861Submitting Author's Institution
NISTSubmitting Author's Country
- United States of America