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

• URL: http://arxiv.org/abs/2605.01231v1
• Date: Sat, 02 May 2026 04:08:51 GMT
• Title: CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models
• Abstract summary: We argue that progress requires a shift from model selection to modular attribution.<n>We propose CombinationTS, a self-contained probabilistic evaluation framework.<n>We show that explicit structural priors yield a more favorable performance-stability trade-off than increasing complexity.
• Score: 15.276272594513427
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Position: It's Time to Optimize LLMs for Self-Consistency [50.68587056599734]
  Many important failures persist in language model pre- and post-training pipelines.<n>We argue that these failures arise from a modeling assumption permeating all aspects of the pipeline.<n>We propose self-consistency as a framework for understanding these failures.
  arXiv  Detail & Related papers  (2026-07-31T19:16:53Z)
• Agentic Designer: Progressive Multi-Agent Collaboration for Structure-Aware Interior Layout Generation [84.5255199507027]
  Agentic Designer is a progressive framework that formulates structure-aware interior layout generation as an iterative and constraint-verified decision process.<n>By layout decomposing into modular stages of proposal, verification, synthesis and adjustment, the framework coordinates three specialized agents, a Generator, an Evaluator, and a Refiner.<n>This mechanism enforces stepwise geometric validation and correction before each placement is committed, thereby preventing error accumulation.
  arXiv  Detail & Related papers  (2026-07-23T02:46:11Z)
• Semi-structured multi-state delinquency model for mortgage default [7.0177704222466994]
  We propose a semi-structured discrete-time multi-state model to analyse mortgage delinquency transitions.<n>This model combines an easy-to-understand structured additive predictor, with a flexible neural network component that captures complex nonlinearities and higher-order interactions.
  arXiv  Detail & Related papers  (2026-03-27T11:22:22Z)
• Steerable Instruction Following Coding Data Synthesis with Actor-Parametric Schema Co-Evolution [30.919665619254]
  We propose IFCodeEvolve, a framework for synthesizing large-scale instruction-paired coding data.<n>We show that IFCodeEvolve significantly boosts base model performance, with our 32B model achieving parity with proprietary SOTA models.<n>We also contribute IFCodeBench, a comprehensive human-verified benchmark equipped with solutions and robust AST-based verification.
  arXiv  Detail & Related papers  (2026-02-27T21:31:41Z)
• SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs [3.036179638516407]
  We introduce SEED, a structural encoder for embedding-driven decoding, which integrates four stages: a token-aware encoder for patch extraction, a projection module that aligns patches with language model embeddings, and a semantic reprogramming mechanism that maps patches to task-aware prototypes.<n>This modular architecture decouples representation learning from inference, enabling efficient alignment between numerical patterns and semantic reasoning.
  arXiv  Detail & Related papers  (2025-06-25T06:40:14Z)
• COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement [80.18490952057125]
  Iterative refinement has emerged as an effective paradigm for enhancing the capabilities of large language models (LLMs) on complex tasks. We propose Context-Wise Order-Agnostic Language Modeling (COrAL) to overcome these challenges. Our approach models multiple token dependencies within manageable context windows, enabling the model to perform iterative refinement internally.
  arXiv  Detail & Related papers  (2024-10-12T23:56:19Z)
• Investigating the Role of Instruction Variety and Task Difficulty in Robotic Manipulation Tasks [50.75902473813379]
  This work introduces a comprehensive evaluation framework that systematically examines the role of instructions and inputs in the generalisation abilities of such models. The proposed framework uncovers the resilience of multimodal models to extreme instruction perturbations and their vulnerability to observational changes.
  arXiv  Detail & Related papers  (2024-07-04T14:36:49Z)
• Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery [62.43562856605473]
  We argue for the computational advantages of a recurrent architecture with complex-valued weights. We propose a fully convolutional autoencoder, SynCx, that performs iterative constraint satisfaction.
  arXiv  Detail & Related papers  (2024-05-27T15:47:03Z)
• Disentanglement via Latent Quantization [60.37109712033694]
  In this work, we construct an inductive bias towards encoding to and decoding from an organized latent space. We demonstrate the broad applicability of this approach by adding it to both basic data-re (vanilla autoencoder) and latent-reconstructing (InfoGAN) generative models.
  arXiv  Detail & Related papers  (2023-05-28T06:30:29Z)
• Self-Reflective Variational Autoencoder [21.054722609128525]
  Variational Autoencoder (VAE) is a powerful framework for learning latent variable generative models. We introduce a solution, which we call self-reflective inference. We empirically demonstrate the clear advantages of matching the variational posterior to the exact posterior.
  arXiv  Detail & Related papers  (2020-07-10T05:05:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.