  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.
