Cluster-and-Conquer: A Framework For Time-Series Forecasting

TL;DR

The paper introduces a flexible, efficient three-stage framework for high-dimensional time-series forecasting that clusters similar series to improve prediction accuracy, supported by theoretical guarantees and state-of-the-art experimental results.

Contribution

It presents a novel, general framework that combines clustering and forecasting, with theoretical analysis and competitive empirical performance.

Findings

Achieves state-of-the-art results on benchmark datasets.

Provides theoretical guarantees in a mixed linear regression setting.

Demonstrates efficiency and parallelizability of the approach.

Abstract

We propose a three-stage framework for forecasting high-dimensional time-series data. Our method first estimates parameters for each univariate time series. Next, we use these parameters to cluster the time series. These clusters can be viewed as multivariate time series, for which we then compute parameters. The forecasted values of a single time series can depend on the history of other time series in the same cluster, accounting for intra-cluster similarity while minimizing potential noise in predictions by ignoring inter-cluster effects. Our framework -- which we refer to as "cluster-and-conquer" -- is highly general, allowing for any time-series forecasting and clustering method to be used in each step. It is computationally efficient and embarrassingly parallel. We motivate our framework with a theoretical analysis in an idealized mixed linear regression setting, where we provide guarantees on the quality of the estimates. We accompany these guarantees with experimental results that demonstrate the advantages of our framework: when instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets, sometimes outperforming deep-learning-based approaches.