# Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks

arXiv: https://arxiv.org/abs/2003.12162 · HTML: https://mlsanity.com/paper/2003.12162/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2020.

## Built on by (most cited first, 10 of 10)

- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [Chronos-2: From Univariate to Universal Forecasting](https://mlsanity.com/paper/2510.15821/) (2025)
- [ForecastPFN: Synthetically-Trained Zero-Shot Forecasting](https://mlsanity.com/paper/2311.01933/) (2023)
- [Retrieval Augmented Time Series Forecasting](https://mlsanity.com/paper/2411.08249/) (2024)
- [TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data](https://mlsanity.com/paper/2502.16294/) (2025)
- [SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction](https://mlsanity.com/paper/2405.15677/) (2024)
- [Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting](https://mlsanity.com/paper/2411.15743/) (2024)
- [MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction](https://mlsanity.com/paper/2505.21553/) (2025)
- [Introducing "Forecast Utterance" for Conversational Data Science](https://mlsanity.com/paper/2309.03877/) (2023)
- [Deep Double Descent for Time Series Forecasting: Avoiding Undertrained Models](https://mlsanity.com/paper/2311.01442/) (2023)

## Abstract (arXiv)

Recurrent neural networks (RNNs) are state-of-the-art in several sequential learning tasks, but they often require considerable amounts of data to generalise well. For many time series forecasting (TSF) tasks, only a few dozens of observations may be available at training time, which restricts use of this class of models. We propose a novel RNN-based model that directly addresses this problem by learning a shared feature embedding over the space of many quantised time series. We show how this enables our RNN framework to accurately and reliably forecast unseen time series, even when there is little to no training data available.