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.

OFTER 是一种面向中等规模多元时间序列的时间序列预测流水线，具有解释性输出，使用了 k 最近邻和广义回归神经网络的非参数模型，以及一种基于加权范数的降维组件。该方法能够应用于低信噪比环境，并且能够优于多种基线模型。