Title: Order-Preserving Pattern Mining Enhances Structure-Aware Time Series Forecasting
Primary area: learning on time series and dynamical systems
Keywords: Time Series, Frequent Order-Preserving Patterns, Loss Function, Machine Learning, Data Mining

Abstract: Traditional time series forecasting models tend to focus on numerical fitting, making it difficult to explicitly model and leverage the relative ordering patterns inherent in time series. This often results in suboptimal predictions when dealing with data segments that exhibit clear pattern regularities. To address this gap, this paper introduces Order-Preserving Patterns (OPPs) into time series forecasting for the first time and proposes a novel model that explicitly incorporates prior pattern knowledge by leveraging frequent OPPs as explicit priors. The proposed model utilizes a convolutional neural network to perform feature dimensionality reduction on high-dimensional labeled time series, extracting one-dimensional representations suitable for pattern mining. It then applies a sliding window and support counting strategy to discover frequent OPPs. An OPP matching mechanism is proposed to distinguish between OPP and non-OPP training samples. Additionally, a pattern constrained loss function is designed to guide the predicted values toward consistency with the prior pattern logic. This constraint is imposed from three perspectives—right boundary, left boundary, and intermediate positions—to ensure order alignment with the tail elements of the OPPs. Experimental results show that, on the ‘Perturbation Boundary’ of the SWAT and ETTh1 datasets, the proposed model achieves reductions of at least 31.45% and 4.35% compared with the SOTA models, respectively. The improvement becomes more pronounced when the window size exceeds the ‘Perturbation Boundary’. Code is available at this repository: https://anonymous.4open.science/r/OPPCL-B070/.

TL;DR: To the best of our knowledge, This is the first study that embeds intrinsic pattern structures into the learning process, underscoring the potential of structural priors in improving the stability and accuracy of deep forecasting models.