Today, we're diving into an innovative approach in time-series forecasting called SeqFusion. Unlike traditional methods that require tons of in-task data, SeqFusion leverages a sequential fusion of diverse pre-trained models (PTMs) to predict future values in a zero-shot setting—meaning it doesn’t need additional training data for each new task. By assessing the specific temporal characteristics of a target time series, SeqFusion selects the most suitable models from a pre-collected batch, performs sequential predictions, and fuses the results into a final forecast, all while safeguarding privacy by using minimal data. As the authors put it, it "fuses diverse pre-trained models... with minimal data to protect privacy," demonstrating competitive accuracy when compared to state-of-the-art forecasting methods.

Key Points:

• Zero-Shot Forecasting: SeqFusion directly predicts future values without extra in-task training data.
• Sequential Fusion of PTMs: It sequentially selects and combines specialized pre-trained models based on the target series' temporal characteristics.
• Privacy-Focused: The method uses minimal data, addressing privacy concerns typically associated with extensive pre-training data collection.
• Performance: Experiments show that SeqFusion achieves competitive accuracy compared to current state-of-the-art forecasting techniques.
• Unique Approach: Instead of gathering diverse pre-training data, this strategy measures distances in a shared representation space to select the optimal PTMs.

This breakthrough method redefines our approach to time-series forecasting and could be a game-changer in real-world applications where data privacy and rapid deployment are key considerations.
Link to Article