用随机爬坡法优化时间序列特征，提升预测效率与可解释性。
Feature Optimization for Time Series Forecasting via Novel Randomized Uphill Climbing
- 基于领域语法随机组合算子生成候选特征程序
- 通过滚动窗口和代理模型快速评分，结合交叉验证过滤不稳定性
- 适合需要快速迭代、低能耗且可解释的预测场景
随机爬坡法（RUC）是一种轻量级、随机搜索启发式方法，在量化对冲基金中已实现顶尖的股票超额收益因子。本文提出将RUC推广为一种模型无关的时间序列特征优化框架，核心思路是通过领域特定语法随机合成候选特征程序，利用廉价代理模型在滚动窗口上快速评分，并通过嵌套交叉验证与信息论收缩过滤不稳定性。该方法将特征发现与高耗能深度学习解耦，有望实现更快的迭代周期、更低的能耗以及更高的可解释性。社会意义：准确且透明的预测工具使资源受限的机构、能源监管方、气候风险类非营利组织能够在无需专有黑箱模型的前提下做出数据驱动决策。
原文摘要 · Abstract (English)
Randomized Uphill Climbing is a lightweight, stochastic search heuristic that has delivered state of the art equity alpha factors for quantitative hedge funds. I propose to generalize RUC into a model agnostic feature optimization framework for multivariate time series forecasting. The core idea is to synthesize candidate feature programs by randomly composing operators from a domain specific grammar, score candidates rapidly with inexpensive surrogate models on rolling windows, and filter instability via nested cross validation and information theoretic shrinkage. By decoupling feature discovery from GPU heavy deep learning, the method promises faster iteration cycles, lower energy consumption, and greater interpretability. Societal relevance: accurate, transparent forecasting tools empower resource constrained institutions, energy regulators, climate risk NGOs to make data driven decisions without proprietary black box models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。