CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models

2026-05-02

AI Summary

This work addresses the often statistically unstable performance gains and unclear attribution in existing time series forecasting models. It proposes CombinationTS, a framework that decouples architectures into five orthogonal modules—input transformation, embedding, encoder, decoder, and output transformation—and quantifies each component’s contribution to both predictive performance (μ) and stability (σ) under a unified evaluation protocol. Through large-scale paired experiments and probabilistic assessment, the study uncovers the “identity paradox”: with effective embeddings, a parameter-free identity encoder can match or even surpass sophisticated backbone encoders. Furthermore, it demonstrates that input transformations incorporating structural priors yield greater benefits than merely increasing encoder complexity. This work establishes a principled baseline for architectural necessity, shifting model evaluation from holistic selection toward fine-grained, component-level attribution.

Abstract

Recent progress in time-series forecasting has led to rapidly increasing architectural complexity, yet many reported State-of-the-Art gains are statistically fragile or misattributed. We argue that progress requires a shift from model selection to modular attribution, identifying which components truly drive performance. We propose CombinationTS, a self-contained probabilistic evaluation framework that decomposes forecasting models into orthogonal modules--Input Transformation, Embedding, Encoder, Decoder, and Output Transformation--and evaluates them under a shared evaluation condition space. By quantifying each component via marginalized performance ($μ$) and stability ($σ$), CombinationTS enables robust attribution beyond fragile point estimates. Through large-scale paired evaluation, we uncover the Identity Paradox: once the data view (Embedding) is well-designed, a parameter-free Identity Encoder often matches or outperforms complex backbones. We further show that explicit structural priors introduced via Input Transformations yield a more favorable performance-stability trade-off than increasing Encoder complexity, establishing a principled baseline for architectural necessity.

Problem

• time-series forecasting
• architectural complexity
• performance attribution
• model robustness
• component evaluation

Innovation

• modular attribution
• time-series forecasting
• CombinationTS
• Identity Paradox
• performance-stability trade-off