Missing Value Imputation in Time Series Using Top-k Case Matching

Abstract

In this paper, we present a simple yet effective algorithm, called the Top-k Case Matching algorithm, for the imputation of miss- ing values in streams of time series data that are similar to each other. The key idea of the algorithm is to look for the k situations in the historical data that are most similar to the current situation and to derive the missing value from the measured values at these k time points. To efficiently identify the top-k most similar historical situations, we adopt Fagin’s Threshold Algorithm, yielding an al- gorithm with sub-linear runtime complexity with high probability, and linear complexity in the worst case (excluding the initial sort- ing of the data, which is done only once). We provide the results of a first experimental evaluation using real-world meteorological data. Our algorithm achieves a high accuracy and is more accurate and efficient than two more complex state of the art solutions.