Rethinking Remaining Useful Life Prediction with Scarce Time Series Data: Regression under Indirect Supervision

• URL: http://arxiv.org/abs/2504.09206v1
• Date: Sat, 12 Apr 2025 13:14:35 GMT
• Title: Rethinking Remaining Useful Life Prediction with Scarce Time Series Data: Regression under Indirect Supervision
• Authors: Jiaxiang Cheng, Yipeng Pang, Guoqiang Hu,
• Abstract summary: We introduce a unified framework called parameterized static regression, which takes single points as inputs for regression of target values.<n>Our method demonstrates competitive performance in prediction accuracy when dealing with highly scarce time series data.
• Score: 4.335413713700667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supervised time series prediction relies on directly measured target variables, but real-world use cases such as predicting remaining useful life (RUL) involve indirect supervision, where the target variable is labeled as a function of another dependent variable. Trending temporal regression techniques rely on sequential time series inputs to capture temporal patterns, requiring interpolation when dealing with sparsely and irregularly sampled covariates along the timeline. However, interpolation can introduce significant biases, particularly with highly scarce data. In this paper, we address the RUL prediction problem with data scarcity as time series regression under indirect supervision. We introduce a unified framework called parameterized static regression, which takes single data points as inputs for regression of target values, inherently handling data scarcity without requiring interpolation. The time dependency under indirect supervision is captured via a parametrical rectification (PR) process, approximating a parametric function during inference with historical posteriori estimates, following the same underlying distribution used for labeling during training. Additionally, we propose a novel batch training technique for tasks in indirect supervision to prevent overfitting and enhance efficiency. We evaluate our model on public benchmarks for RUL prediction with simulated data scarcity. Our method demonstrates competitive performance in prediction accuracy when dealing with highly scarce time series data.

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