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

In this paper, we present a simple yet effective algorithm, called the Top-k Case Matching algorithm, for the imputation of missing 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 algorithm with sub-linear runtime complexity with high probability, and linear complexity in the worst case (excluding the initial sorting 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.