  Errors are prevalent in time series data, such as GPS trajectories or sensor
readings. Existing methods focus more on anomaly detection but not on repairing
the detected anomalies. By simply filtering out the dirty data via anomaly
detection, applications could still be unreliable over the incomplete time
series. Instead of simply discarding anomalies, we propose to (iteratively)
repair them in time series data, by creatively bonding the beauty of temporal
nature in anomaly detection with the widely considered minimum change principle
in data repairing. Our major contributions include: (1) a novel framework of
iterative minimum repairing (IMR) over time series data, (2) explicit analysis
on convergence of the proposed iterative minimum repairing, and (3) efficient
estimation of parameters in each iteration. Remarkably, with incremental
computation, we reduce the complexity of parameter estimation from O(n) to
O(1). Experiments on real datasets demonstrate the superiority of our proposal
compared to the state-of-the-art approaches. In particular, we show that (the
proposed) repairing indeed improves the time series classification application.
