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Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
Xvyuan Liu, Xiangfei Qiu, Xingjian Wu, Zhengyu Li, Chenjuan Guo, Jilin Hu, Bin Yang
TL;DR
IMTS forecasting is challenged by irregular, missing observations and the high cost of many existing models. APN uses TAPA to adaptively aggregate observations into regularized representations, then applies lightweight modules; across multiple real-world datasets, it outperforms existing state-of-the-art methods in efficiency and accuracy.
Problem
Irregular observations, missing data, and the computational cost of existing models make accurate and practical IMTS forecasting challenging.
Method
APN uses TAPA to learn dynamic patch boundaries and time-aware aggregation, followed by a simple query module and shallow MLP.
Results
APN outperforms existing state-of-the-art methods in both forecasting efficiency and accuracy on multiple real-world datasets.
Takeaways & Limitations
Direct aggregation of original observations provides regularized patch representations without interpolation, preserving data fidelity and covering relevant observations.
Abstract
from arXiv · showhide
The forecasting of irregular multivariate time series (IMTS) is crucial in key areas such as healthcare, biomechanics, climate science, and astronomy. However, achieving accurate and practical predictions is challenging due to two main factors. First, the inherent irregularity and data missingness in irregular time series make modeling difficult. Second, most existing methods are typically complex and resource-intensive. In this study, we propose a general framework called APN to address these challenges. Specifically, we design a novel Time-Aware Patch Aggregation (TAPA) module that achieves adaptive patching. By learning dynamically adjustable patch boundaries and a time-aware weighted averaging strategy, TAPA transforms the original irregular sequences into high-quality, regularized representations in a channel-independent manner. Additionally, we use a simple query module to effectively integrate historical information while maintaining the model's efficiency. Finally, predictions are made by a shallow MLP. Experimental results on multiple real-world datasets show that APN outperforms existing state-of-the-art methods in both efficiency and accuracy.
1 Introduction
IMTS forecasting is difficult because observations are irregular and incomplete, while many existing models are computationally costly. APN addresses both challenges with adaptive patching and a lightweight forecasting architecture.
- IMTS data arise across healthcare, biomechanics, climate science, and astronomy, motivating forecasting for early warning and proactive decision-making.
- Fixed patching can mismatch local information density, producing insufficient features in sparse regions and redundant or noisy features in dense regions.
- Fixed patching can also split critical dynamic information, preventing the model from capturing complete semantic context.
- Neural-ODE, GNN, and Transformer-based IMTSF models often require costly numerical solvers, graph operations, or large parameter scales.
- APN combines TAPA adaptive patching, a simple query module, and a shallow MLP to produce regularized representations and predictions efficiently.
- APN outperforms existing state-of-the-art baselines in forecasting accuracy and computational efficiency across multiple datasets.
2 Related Work
Related IMTS forecasting methods use continuous-time dynamics, graph-based modeling, attention, or fixed patching, but these approaches face efficiency or segmentation limitations. APN instead aggregates original observations through adaptive soft windows.
- Continuous-time IMTSF methods use ODEs or SDEs to model irregular sampling, with some approaches reducing or avoiding costly numerical integration.
- Initial patch-based IMTSF methods use fixed-span patches, which are poorly suited to IMTS with non-uniform data distributions.
- Interpolation-based adaptive patching can fabricate misleading observations across sparse or missing intervals, introducing artifacts and reducing reliability.
- TAPA learns dynamic soft windows and directly aggregates original observations, preserving data fidelity while ensuring complete information coverage.
3 Methodology
APN separates irregularity handling from forecasting by using TAPA to convert irregular observations into regularized patch representations, followed by lightweight contextual aggregation and decoding. TAPA learns dynamic temporal windows and aggregates original observations with time-aware weights, preserving information while adapting to local density.
- Framework Overview: APN uses a two-stage pipeline: TAPA regularizes irregular observations, then query-based aggregation and an MLP decoder produce forecasts.The design front-loads irregularity handling into TAPA so later modules remain lightweight.
- Weighted Aggregation: Each patch representation is computed by directly aggregating all original observations with weights determined by temporal relevance.This two-step process converts each irregular input sequence into a regularized sequence of patch representations.
- Adaptive Patching: TAPA learns dynamic temporal windows independently for each channel instead of imposing rigid patch boundaries.Window centers and widths adapt to the non-uniform distribution of observations.
- Weighted Aggregation: TAPA enriches observations with learnable temporal embeddings before its time-aware soft-window weighting constructs patch representations.The weighting function uses two Sigmoid terms to form a continuous influence curve with adjustable edge softness.
- Forecasting Decoder: A shallow two-layer MLP decoder combines the aggregated summary with the query time encoding to produce the final predicted value.The simple decoder is presented as evidence that TAPA and query aggregation provide rich representations.
- Weighted Aggregation: The soft window assigns positive weights to every observation, guaranteeing complete information coverage while varying each observation’s influence.Unlike hard segmentation, this prevents observations from being discarded because of patch boundaries.
4 Experiments
Experiments evaluate APN on four IMTS datasets, including forecasting, ablation, sensitivity, and computational-efficiency analyses. APN shows strong efficiency results, while performance is especially sensitive to the temporal embedding dimension.
- Experimental setup: APN is evaluated on PhysioNet, MIMIC, HumanActivity, and USHCN using standard train, validation, and test splits.The datasets cover healthcare, biomechanics, and climate science.
- Experimental setup: Forecasting performance is assessed with MSE and MAE, while ablations report MSE as mean ± std.Table 2 marks the best and second-best results, whereas Table 3 highlights the best ablation results.
- Parameter sensitivity: APN performance is especially sensitive to Dte, with overly large values degrading results on PhysioNet and MIMIC.A moderate Dte provides effective temporal information, whereas larger values may introduce noise.
- Parameter sensitivity: Model performance is generally robust to the number of patches P across a reasonable range for most datasets.This suggests local features remain effectively captured across varied patch counts.
- Scalability and efficiency analysis: APN shows significant advantages over GraFITi, CRU, and tPatchGNN across peak GPU memory, parameters, training time, and inference time on USHCN with batch size 32.Each efficiency metric uses lower-is-better comparisons.
5 Conclusion
The conclusion presents APN as an efficient IMTS forecasting framework centered on adaptive aggregation rather than fixed patching or interpolation. Its regularized representations support concise prediction architecture, and experiments report gains in accuracy and computational efficiency.
- 5 Conclusion: APN uses TAPA to learn dynamic boundaries and time-aware soft weights for aggregating raw observations into regularized patch representations.The module provides full data coverage and handles variations in information density.
- 5 Conclusion: A concise query module and shallow MLP use the resulting representations to make forecasts.
- 5 Conclusion: Experiments on multiple public IMTS benchmarks show APN significantly outperforms existing state-of-the-art methods in prediction accuracy and computational efficiency.