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.

翻译：与传统时间序列预测方法需要大量任务内数据进行训练不同，零样本预测能够在给定目标时间序列的情况下直接预测未来值，而无需额外的训练数据。当前的零样本方法主要依赖于预训练的通用模型，其性能通常取决于预训练数据的多样性和相关性，这可能引发隐私担忧。本研究没有收集多样化的预训练数据，而是引入了SeqFusion，这是一个新颖的框架，它顺序地收集并融合多样化的预训练模型（PTMs）以进行零样本预测。基于目标时间序列的具体时序特征，SeqFusion从一批预先收集的PTMs中选择最合适的模型，执行顺序预测，并在使用最少数据以保护隐私的同时融合所有预测结果。这些PTMs各自专精于不同的时序模式和预测任务，使得SeqFusion能够通过测量目标时间序列与每个PTM在共享表示空间中的距离来进行选择。实验表明，与最先进的方法相比，SeqFusion在零样本预测中实现了具有竞争力的准确性。