Multi-task Meta Label Correction for Time Series Prediction

arXiv:2303.08103v3 Announce Type: replace
Abstract: Time series classification faces two unavoidable problems. One is partial feature information and the other is poor label quality, which may affect model performance. To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework. There are three main contributions. First, we train the label correction model with a two-branch neural network in the outer loop. While in the model-agnostic inner loop, we use pre-existing …

abstract arxiv classification cs.ce cs.lg data feature framework information meta meta-learning performance prediction quality series time series type