Position: The Inevitable End of One-Architecture-Fits-All-Domains in Time Series Forecasting

2026-02-02

AI Summary

This study addresses the performance limitations of existing general-purpose neural network architectures for time series forecasting when applied across diverse domains, as they struggle to meet the distinct requirements of fields such as finance, meteorology, and transportation. Through a systematic literature review and trend analysis, this work is the first to demonstrate the unsustainability of the prevailing paradigm that a single architecture can universally excel across all domains, revealing that performance gains plateau despite increasing model complexity. The findings advocate a strategic shift toward domain-specific deep learning architectures or meta-learning frameworks, offering the research community a clear direction to advance time series forecasting from generic solutions toward domain-adapted and adaptive learning approaches.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Recent work has questioned the effectiveness and robustness of neural network architectures for time series forecasting tasks. We summarize these concerns and analyze groundly their inherent limitations: i.e. the irreconcilable conflict between single (or few similar) domains SOTA and generalizability over general domains for time series forecasting neural network architecture designs. Moreover, neural networks architectures for general domain time series forecasting are becoming more and more complicated and their performance has almost saturated in recent years. As a result, network architectures developed aiming at fitting general time series domains are almost not inspiring for real world practices for certain single (or few similar) domains such as Finance, Weather, Traffic, etc: each specific domain develops their own methods that rarely utilize advances in neural network architectures of time series community in recent 2-3 years. As a result, we call for the time series community to shift focus away from research on time series neural network architectures for general domains: these researches have become saturated and away from domain-specific SOTAs over time. We should either (1) focus on deep learning methods for certain specific domain(s), or (2) turn to the development of meta-learning methods for general domains.

Problem

• time series forecasting
• neural network architecture
• domain-specific
• generalizability
• model saturation

Innovation

• time series forecasting
• domain-specific models
• neural architecture saturation
• meta-learning
• one-architecture-fits-all