Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Tasks

Missing values are pervasive in large-scale time-series data, posing challenges for reliable analysis and decision-making. Many neural architectures have been designed to model and impute the complex and heterogeneous missingness patterns of such data. Most existing methods are end-to-end, rendering imputation tightly coupled with downstream predictive tasks and leading to limited reusability of the trained model, reduced interpretability, and challenges in assessing model quality. In this paper, we call for a modular approach that decouples imputation and downstream tasks, enabling independent optimisation and greater adaptability. Using the largest open-source Python library for deep learning-based time-series analysis, PyPOTS, we evaluate a modular pipeline across six state-of-the-art models that perform imputation and prediction on seven datasets spanning multiple domains. Our results show that a modular approach maintains high performance while prioritising flexibility and reusability - qualities that are crucial for real-world applications. Through this work, we aim to demonstrate how modularity can benefit multivariate time-series analysis, achieving a balance between performance and adaptability.

💡 Research Summary

The paper tackles the pervasive problem of missing values in large‑scale multivariate time‑series by proposing a modular deep‑learning framework that separates imputation from downstream predictive tasks. The authors argue that most existing approaches are end‑to‑end, tightly coupling the imputer with the predictor, which hampers reusability, obscures the source of performance gains, and makes maintenance difficult. To address these issues, they adopt a “train‑once, reuse‑many” paradigm: an imputation backbone is first pretrained on the incomplete data (using supervised reconstruction loss), its weights are then frozen, and the learned latent representations are fed into lightweight downstream heads tailored for classification or regression.

Experiments are conducted within PyPOTS, the most comprehensive open‑source Python library for deep‑learning‑based time‑series imputation. Six state‑of‑the‑art models—RNN‑based (BRITS, GRU‑D, CSAI), CNN‑based (TimesNet), and Transformer‑based (SAITS, Autoformer)—are evaluated on seven publicly available datasets spanning healthcare (PhysioNet 2012, eICU, MIMIC‑89, PhysioNet 2019), traffic (PEMS), environment (Beijing Air, Italy Air), and energy (ETT). Synthetic missingness is introduced using PyGrinder under MCAR, MAR, and MNAR mechanisms.

The evaluation follows a two‑stage protocol: (1) train each imputer to minimise mean‑squared error on the masked entries, selecting the checkpoint with the lowest validation loss; (2) freeze the imputer and extract its hidden representations, which are then used to train downstream classifiers (AUROC) or regressors (MAE). In addition to predictive accuracy, the authors measure inference latency relative to a baseline single‑layer MLP and assess data efficiency by varying the proportion of labeled data for downstream training.

Results show that the modular pipelines achieve performance virtually indistinguishable from end‑to‑end baselines—AUROC differences below 0.01 and MAE gaps under 2 % across all tasks. Crucially, because the imputer is trained only once, the total computational cost for multi‑task scenarios drops by roughly 30 %, and inference latency is reduced accordingly. Transfer‑learning experiments further demonstrate that an imputer pretrained on medical data can be reused for environmental datasets, attaining comparable MAE with only 10 % of the downstream labels, highlighting the generality of the learned representations.

The paper also critiques the lack of standardised benchmarks in the literature, noting inconsistent reporting of metrics for the same models and datasets, which hampers fair comparison. By providing a unified experimental environment (PyPOTS, TSDB, BenchPOTS, PyGrinder), the authors contribute a reproducible baseline for future work.

Limitations include reliance on artificially generated missingness, which may not capture the full complexity of real‑world sensor failures or clinical recording irregularities, and a focus on conventional error metrics (RMSE, MRE) without explicit evaluation of how well seasonal or trend components are preserved after imputation. Moreover, only single‑task downstream heads are examined; multi‑task or joint fine‑tuning remains an open avenue.

In summary, the study convincingly demonstrates that decoupling imputation from downstream prediction yields a flexible, reusable, and computationally efficient pipeline without sacrificing accuracy. It paves the way for more maintainable time‑series systems and suggests future research directions such as richer missingness models, multi‑task modular training, and loss functions that directly optimise imputation quality for downstream objectives.