We introduce OFTER, a time series forecasting pipeline tailored for mid-sized multivariate time series. OFTER utilizes the non-parametric models of k-nearest neighbors and Generalized Regression Neural Networks, integrated with a dimensionality reduction component. To circumvent the curse of dimensionality, we employ a weighted norm based on a modified version of the maximal correlation coefficient. The pipeline we introduce is specifically designed for online tasks, has an interpretable output, and is able to outperform several state-of-the art baselines. The computational efficacy of the algorithm, its online nature, and its ability to operate in low signal-to-noise regimes, render OFTER an ideal approach for financial multivariate time series problems, such as daily equity forecasting. Our work demonstrates that while deep learning models hold significant promise for time series forecasting, traditional methods carefully integrating mainstream tools remain very competitive alternatives with the added benefits of scalability and interpretability.

翻译：我们提出OFTER，一种专为中规模多变量时间序列设计的时间序列预测流水线。OFTER采用k近邻与广义回归神经网络的非参数模型，并集成降维组件。为避免维数灾难，我们基于最大相关系数的修正版本引入加权范数。该流水线专为在线任务设计，输出具有可解释性，且能超越多个当前最优基线方法。其计算效率、在线特性及在低信噪比环境下的运行能力，使OFTER成为金融多变量时间序列问题（如日频股票预测）的理想方案。本研究表明，尽管深度学习模型在时间序列预测中潜力显著，但通过精心整合主流工具的传统方法仍具备可扩展性与可解释性的额外优势，保持强劲竞争力。