Abstract
Historical data are frequently involved in situations where the available reports on time series are temporally aggregated at different levels, e.g., themonthlycounts of people infected with measles. In real databases, the time periods covered by different reports can have overlaps (i.e., time-ticks covered by more than one reports) or gaps (i.e., time-ticks not covered by any report). However, data analysis and machine learning models require reconstructing the historical events in a finer granularity, e.g., the weekly patient counts, for elaborate analysis and prediction. Thus, data disaggregation algorithms are becoming increasingly important in various domains. Time series disaggregation methods commonly utilize domain knowledge about the data, e.g., smoothness, periodicity, or sparsity, to improve the reconstruction accuracy. In this paper, we propose a novel approach, calledTurboLift, which aims to improve the quality of the solutions provided by existing disaggregation methods. Starting from a solution produced by a specific method,TurboLiftfinds a new solution that reduces the disaggregation error and is close to the initial one. We derive a closed-form solution to the proposed formulation ofTurboLiftthat enables us to obtain an accurate reconstruction analytically, without performing resource and time-consuming iterations. Experiments on real data from different domains showcase the effectiveness ofTurboLiftin terms of disaggregation error, and outlier and anomaly detection.