Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting

TL;DR

This paper introduces Freq-Synth, a frequency-driven synthetic data generation framework that enhances zero-shot and few-shot time series forecasting by addressing frequency learning challenges.

Contribution

It proposes a novel Fourier analysis-based method to understand model learning issues and introduces Freq-Synth to improve forecasting robustness with limited data.

Findings

Models struggle with multiple and unseen frequencies.

Freq-Synth improves forecasting accuracy in low-data scenarios.

Synthetic data enhances model generalization to unseen frequencies.

Abstract

Time series forecasting is critical in numerous real-world applications, requiring accurate predictions of future values based on observed patterns. While traditional forecasting techniques work well in in-domain scenarios with ample data, they struggle when data is scarce or not available at all, motivating the emergence of zero-shot and few-shot learning settings. Recent advancements often leverage large-scale foundation models for such tasks, but these methods require extensive data and compute resources, and their performance may be hindered by ineffective learning from the available training set. This raises a fundamental question: What factors influence effective learning from data in time series forecasting? Toward addressing this, we propose using Fourier analysis to investigate how models learn from synthetic and real-world time series data. Our findings reveal that forecasters commonly suffer from poor learning from data with multiple frequencies and poor generalization to unseen frequencies, which impedes their predictive performance. To alleviate these issues, we present a novel synthetic data generation framework, designed to enhance real data or replace it completely by creating task-specific frequency information, requiring only the sampling rate of the target data. Our approach, Freq-Synth, improves the robustness of both foundation as well as nonfoundation forecast models in zero-shot and few-shot settings, facilitating more reliable time series forecasting under limited data scenarios.