Why Model Selection Fails in Time Series Forecasting: An Empirical Study of Instability Across Data Regimes
时间序列预测中的模型选择为何失效：跨数据状态的不稳定性AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接研究时间序列预测及预测模型选择，未说明应用限于宏观数据。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
时间序列预测中的模型选择为何失效：跨数据状态的不稳定性
- 研究问题
- 趋势、季节性、噪声和时间依赖等简单序列特征，能否可靠指引预测模型选择，其表现为何随数据状态与预测期限发生变化？
- 方法与思路
- 作者构建可测量的数据状态描述指标，将其映射为候选预测模型，并在多个真实数据集和不同预测期限下比较推荐与实际表现。
- 材料陈述的贡献
- 摘要称规则式选择的正确识别比例较低，模型排序随数据及期限明显变化，为静态特征规则难以跨情境推广提供经验依据。
- 阅读关注点
- 应核对数据集及候选模型范围、最优模型的评判标准、训练验证划分，以及特征计算是否泄漏信息或影响跨期限比较。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给出数据清单和准确率，不能由所测规则失效推断所有模型选择方法均失效。
原文摘要
Time series forecasting models often exhibit inconsistent performance across datasets with varying statistical and structural properties. Despite the wide range of available forecasting techniques, it remains unclear whether model selection can be reliably guided by simple data characteristics. This paper investigates why rule-based model selection fails in time series forecasting by analyzing the relationship between data-regime descriptors and model performance. A descriptor-based framework is introduced to characterize time series using measurable properties, including trend strength, seasonality, noise level, and temporal dependence. Based on these descriptors, a rule-based selection mechanism is formulated to map data regimes to candidate forecasting models. The approach is evaluated on multiple real-world datasets across different domains and forecasting horizons. The results show that rule-based model selection achieves low accuracy, with correct model identification occurring in only a small fraction of cases. Significant discrepancies are observed between recommended and empirically optimal models, particularly in noisy and mixed regimes. Further analysis reveals that model performance is highly sensitive to both dataset characteristics and forecasting horizon, resulting in substantial ranking instability across scenarios. These findings explain why simple heuristic rules fail to generalize and demonstrate that forecasting performance cannot be reliably predicted using static, descriptor-based approaches. This study provides empirical evidence that model selection in time series forecasting is inherently context-dependent and highlights the need for more adaptive, data-driven strategies.