Cluster-and-Conquer: A Framework For Time-Series Forecasting
This addresses forecasting challenges in high-dimensional time-series data, offering a general and efficient framework, though it is incremental as it builds on existing forecasting and clustering methods.
The paper tackles forecasting high-dimensional time-series data by proposing a three-stage framework that clusters time series based on estimated parameters to account for intra-cluster similarities while ignoring inter-cluster noise, achieving state-of-the-art results on benchmark datasets, sometimes outperforming deep-learning approaches.
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.