You are here:
A novel imputation methodology for time series based on pattern sequence forecasting
Citation:
Bokde, N., M. Beck, F. Alvarez, AND K. Kulat. A novel imputation methodology for time series based on pattern sequence forecasting. Pattern Recognition Letters. Elsevier, AMSTERDAM, Netherlands, 116:88-96, (2018). https://doi.org/10.1016/j.patrec.2018.09.020
Impact/Purpose:
This manuscript describes a statistical method to impute missing observations in time series data. The method was compared with alternative techniques to demonstrate improved precision. The new technique will have value for improving methods of analysis for time series data, which is applicable across many disciplines.
Description:
The Pattern Sequence Forecasting (PSF) algorithm is a previously described algorithm that identifies patterns in time series data and forecasts values using periodic characteristics of the observations. A new method for univariate time series is introduced that modifies the PSF algorithm to simultaneously forecast and backcast missing values for imputation. The imputePSF method extends PSF by characterizing repeating patterns of existing observations to provide a more precise estimate of missing values compared to more conventional methods, such as replacement with means or last observation carried forward. The imputation accuracy of imputePSF was evaluated by simulating varying amounts of missing observations with three univariate datasets. Comparisons of imputePSF with well-established methods using the same simulations demonstrated an overall reduction in error estimates. The imputePSF algorithm can produce more precise imputations on appropriate datasets, particularly those with periodic and repeating patterns.
URLs/Downloads:
DOI: A novel imputation methodology for time series based on pattern sequence forecasting