OFTER: An Online Pipeline for Time Series Forecasting

TL;DR

OFTER is an online, interpretable pipeline for multivariate time series forecasting that combines non-parametric models with dimensionality reduction, outperforming several state-of-the-art methods especially in financial applications.

Contribution

The paper introduces OFTER, a novel online forecasting pipeline integrating k-nearest neighbors and G-RNNs with a new dimensionality reduction technique for improved performance.

Findings

Outperforms several state-of-the-art baselines.

Effective in low signal-to-noise regimes.

Suitable for financial multivariate time series.

Abstract

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.