SeqFusion: Sequential Fusion of Pre-Trained Models for Zero-Shot Time-Series Forecasting

Abstract

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. Current zero-shot approaches primarily rely on pre-trained generalized models, with their performance often depending on the variety and relevance of the pre-training data, which can raise privacy concerns. Instead of collecting diverse pre-training data, we introduce SeqFusion in this work, a novel framework that collects and fuses diverse pre-trained models (PTMs) sequentially for zero-shot forecasting. Based on the specific temporal characteristics of the target time series, SeqFusion selects the most suitable PTMs from a batch of pre-collected PTMs, performs sequential predictions, and fuses all the predictions while using minimal data to protect privacy. Each of these PTMs specializes in different temporal patterns and forecasting tasks, allowing SeqFusion to select by measuring distances in a shared representation space of the target time series with each PTM. Experiments demonstrate that SeqFusion achieves competitive accuracy in zero-shot forecasting compared to state-of-the-art methods.