Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks
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.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionTime SeriesTime Series AnalysisTime Series ForecastingSimilar Papers 제목 키워드 기반
Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction
We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. GTT is pretrained on a large dataset of 200M high-quality time series samples spanning…
Multivariate Time Series ForecastingTime SeriesTime Series ForecastingSeqFusion: Sequential Fusion of Pre-Trained Models for Zero-Shot Time-Series Forecasting
Unlike traditional time-series forecasting methods that require extensive in-task data for training, zero-shot forecasting can directly predict future values given a target time series without additional training data. C…
Time SeriesTime Series ForecastingReverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
Learning time series foundation models has been shown to be a promising approach for zero-shot time series forecasting across diverse time series domains. Insofar as scaling has been a critical driver of performance of f…
Time Series ForecastingData AugmentationShapelets-Enriched Selective Forecasting using Time Series Foundation Models
Time series foundation models have recently gained a lot of attention due to their ability to model complex time series data encompassing different domains including traffic, energy, and weather. Although they exhibit st…
Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models
Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time-series forecasting. While LLMs have shown potential in ze…
SensitivityTime SeriesTime Series Forecasting