# OFTER: An Online Pipeline for Time Series Forecasting

arXiv: https://arxiv.org/abs/2304.03877 · HTML: https://mlsanity.com/paper/2304.03877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 8 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks](https://mlsanity.com/paper/1609.08409/) (2016)
- [AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction](https://mlsanity.com/paper/1802.08465/) (2018)

## Built on by (most cited first, 1 of 1)

- [Towards Foundation Time Series Model: To Synthesize Or Not To Synthesize?](https://mlsanity.com/paper/2403.02534/) (2024)

## Abstract (arXiv)

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.