Research Article
AutoMTS: Fully Autonomous Processing of Multivariate Time Series Data from Heterogeneous Sensor Networks
@INPROCEEDINGS{10.1007/978-3-030-77569-8_12, author={Ricardo Sousa and Concei\`{e}\"{a}o Amado and Rui Henriques}, title={AutoMTS: Fully Autonomous Processing of Multivariate Time Series Data from Heterogeneous Sensor Networks}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 16th EAI International Conference, QShine 2020, Virtual Event, November 29--30, 2020, Proceedings}, proceedings_a={QSHINE}, year={2021}, month={6}, keywords={Parameter-free learning Multivariate time series Missing values imputation Outlier detection Heterogeneous sensor networks}, doi={10.1007/978-3-030-77569-8_12} }- Ricardo Sousa
Conceição Amado
Rui Henriques
Year: 2021
AutoMTS: Fully Autonomous Processing of Multivariate Time Series Data from Heterogeneous Sensor Networks
QSHINE
Springer
DOI: 10.1007/978-3-030-77569-8_12
Abstract
Heterogeneous sensor networks, including water distribution systems and traffic monitoring systems, produce abundant time series data with an arbitrarily-high multivariate order for monitoring network dynamics and detecting events of interest. Nevertheless, errors and failures in the calibration, data storage or acquisition can occur on some of the sensors installed in those systems, producing missing and/or anomalous values. This work proposes a computational system, referred as AutoMTS, for the fully autonomous cleaning of multivariate time series data using strict quality criteria assessed against ground truth extracted from the targeted series data. The proposed methodology is parameter-free as it relies on robust principles for the assessment, hyperparameterization and selection of methods. AutoMTS coherently supports an extensive set state-of-the-art methods for (multivariate) time series imputation and outlier detection-and-treatment, considering both point and segment/serial occurrences. A comprehensive evaluation of AutoMTS is accomplished using heterogeneous sensors from two water distribution systems with varying sampling rates, water consumption patterns, and inconsistencies. Results confirm the relevance of the proposed AutoMTS system. AutoMTS is provided as an open-source tool available athttps://github.com/RicardoFLNSousa/AutoMTS/tree/master.
