Retail price time series imputation

Abstract

A new method to fill in, or impute, missing prices in retail price time series datasets is proposed, called retail price time series imputation RPTSI. It is constructed from an ensemble of three existing methods: namely, price change lookup, central moving average, and polynomial interpolation. Four extended variations of RPTSI are also proposed by considering historical prices for similar products sold by the same retailer and equivalent products sold by competing retailers. Crowdsourced datasets from four North American cities over a year and a half period were used in experiments to evaluate the five RPTSI-based methods and to compare the results against those obtained using last value carried forward, mean imputation, moving average, polynomial interpolation, and multiple imputation. Accuracy was measured by using mean absolute imputation error. Experimental results showed that the RPTSI-based methods had significantly higher accuracy than the other methods on both univariate and multivariate time series datasets.