Multivariate Time Series Cleaning under Speed Constraints

TL;DR

This paper introduces MTCSC, a novel, efficient method for cleaning multivariate time series data that leverages correlations and data trends, outperforming existing methods in accuracy and speed.

Contribution

The paper presents MTCSC, a linear-time, constraint-based approach for multivariate time series cleaning that incorporates data trends and adaptive speed constraints.

Findings

MTCSC achieves higher repair accuracy than state-of-the-art methods.

MTCSC operates efficiently with less computational time.

Effective even with weak or no correlations between dimensions.

Abstract

Errors are common in time series due to unreliable sensor measurements. Existing methods focus on univariate data but do not utilize the correlation between dimensions. Cleaning each dimension separately may lead to a less accurate result, as some errors can only be identified in the multivariate case. We also point out that the widely used minimum change principle is not always the best choice. Instead, we try to change the smallest number of data to avoid a significant change in the data distribution. In this paper, we propose MTCSC, the constraint-based method for cleaning multivariate time series. We formalize the repair problem, propose a linear-time method to employ online computing, and improve it by exploiting data trends. We also support adaptive speed constraint capturing. We analyze the properties of our proposals and compare them with SOTA methods in terms of effectiveness, efficiency versus error rates, data sizes, and applications such as classification. Experiments on real datasets show that MTCSC can have higher repair accuracy with less time consumption. Interestingly, it can be effective even when there are only weak or no correlations between the dimensions.