Multi-label Prediction in Time Series Data using Deep Neural Networks
- URL: http://arxiv.org/abs/2001.10098v1
- Date: Mon, 27 Jan 2020 21:35:15 GMT
- Title: Multi-label Prediction in Time Series Data using Deep Neural Networks
- Authors: Wenyu Zhang, Devesh K. Jha, Emil Laftchiev, Daniel Nikovski
- Abstract summary: This paper addresses a multi-label predictive fault classification problem for multidimensional time-series data.
The proposed algorithm is tested on two public benchmark datasets.
- Score: 19.950094635430048
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper addresses a multi-label predictive fault classification problem
for multidimensional time-series data. While fault (event) detection problems
have been thoroughly studied in literature, most of the state-of-the-art
techniques can't reliably predict faults (events) over a desired future
horizon. In the most general setting of these types of problems, one or more
samples of data across multiple time series can be assigned several concurrent
fault labels from a finite, known set and the task is to predict the
possibility of fault occurrence over a desired time horizon. This type of
problem is usually accompanied by strong class imbalances where some classes
are represented by only a few samples. Importantly, in many applications of the
problem such as fault prediction and predictive maintenance, it is exactly
these rare classes that are of most interest. To address the problem, this
paper proposes a general approach that utilizes a multi-label recurrent neural
network with a new cost function that accentuates learning in the imbalanced
classes. The proposed algorithm is tested on two public benchmark datasets: an
industrial plant dataset from the PHM Society Data Challenge, and a human
activity recognition dataset. The results are compared with state-of-the-art
techniques for time-series classification and evaluation is performed using the
F1-score, precision and recall.
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