LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling

2026-05-11

AI Summary

This work addresses the limitations of traditional time series forecasting methods, which treat the future as a static target and struggle to capture dynamically evolving non-stationary patterns. The study reframes forecasting as an adaptive multi-step scheduling problem—a novel perspective—and introduces a hierarchical controller that dynamically determines both the prediction scale and step size at each iteration. By integrating neural controlled differential equations, the approach enables hybrid continuous-discrete temporal evolution. This formulation substantially enhances modeling capacity for non-stationary dynamics, yielding average performance improvements of over 7.4% across multiple real-world and synthetic datasets. Moreover, the method achieves 2.6–5.3× faster inference than standard Transformer-based models and offers enhanced interpretability through explicit scheduling trajectories.

Abstract

Time series forecasting serves as an essential tool for many real-world applications, supporting tasks such as resource optimization and decision-making. Despite significant architectural advancements, most modern models still treat forecasting task as a fixed mapping from history to target horizons. This induces temporal decoupling across future time points and limits the model's ability to adapt to the evolving context as forecasting progresses. In this work, we present LeapTS, a novel framework that reformulates time series forecasting as a dynamic scheduling process over the prediction horizon. Specifically, LeapTS organizes the forecasting process into multi-level decisions using: (1) the hierarchical controller to dynamically select the optimal prediction scale and advancement length at each step, and (2) continuous-time state evolution driven by neural controlled differential equations. Within this process, the controlled update mechanism explicitly couples the irregular temporal dynamics with discrete scheduling feedback. Extensive evaluations on both real-world and synthetic datasets demonstrate that LeapTS improves overall forecasting performance by at least 7.4% while achieving a 2.6$\times$ to 5.3$\times$ inference speedup over representative Transformer-based models. Furthermore, by explicitly tracing the scheduling trajectories, we reveal how the model autonomously adapts its forecasting behavior to capture non-stationary dynamics.

Problem

• time series forecasting
• temporal decoupling
• adaptive prediction
• non-stationary dynamics
• multi-horizon scheduling

Innovation

• adaptive scheduling
• neural controlled differential equations
• multi-horizon forecasting
• dynamic prediction scale
• non-stationary time series