Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering

TL;DR

The paper introduces FOCUS, a novel multivariate time series forecasting method that uses offline clustering to identify prototypes, enabling accurate, efficient, and scalable long-range dependency modeling.

Contribution

FOCUS is the first approach to incorporate offline clustering for prototype extraction, reducing complexity and improving accuracy in multivariate time series forecasting.

Findings

Achieves state-of-the-art accuracy on multiple benchmarks.

Reduces computational complexity from quadratic to linear.

Effectively captures high-level event dependencies.

Abstract

Accurate and efficient multivariate time series (MTS) forecasting is essential for applications such as traffic management and weather prediction, which depend on capturing long-range temporal dependencies and interactions between entities. Existing methods, particularly those based on Transformer architectures, compute pairwise dependencies across all time steps, leading to a computational complexity that scales quadratically with the length of the input. To overcome these challenges, we introduce the Forecaster with Offline Clustering Using Segments (FOCUS), a novel approach to MTS forecasting that simplifies long-range dependency modeling through the use of prototypes extracted via offline clustering. These prototypes encapsulate high-level events in the real-world system underlying the data, summarizing the key characteristics of similar time segments. In the online phase, FOCUS dynamically adapts these patterns to the current input and captures dependencies between the input segment and high-level events, enabling both accurate and efficient forecasting. By identifying prototypes during the offline clustering phase, FOCUS reduces the computational complexity of modeling long-range dependencies in the online phase to linear scaling. Extensive experiments across diverse benchmarks demonstrate that FOCUS achieves state-of-the-art accuracy while significantly reducing computational costs.