Déjà vu: A data-centric forecasting approach through time series cross

Accurate forecasts are vital for supporting the decisions of modern companies. Forecasters typically select the most appropriate statistical model for each time series. However, statistical models usually presume some data generation process while making strong assumptions about the errors. In this paper, we present a novel data-centric approach - 'forecasting with cross-similarity', which tackles model uncertainty in a model-free manner. Existing similarity-based methods focus on identifying similar patterns within the series, i.e., 'self similarity'. In contrast, we propose searching for similar patterns from a reference set, i.e., 'cross-similarity'. Instead of extrapolating, the future paths of the similar series are aggregated to obtain the forecasts of the target series. Building on the cross-learning concept, our approach allows the application of similarity-based forecasting on series with limited lengths. We evaluate the approach using a rich collection of real data and show that it yields competitive accuracy in both points forecasts and prediction intervals.

Automated summary

Accurate forecasts are crucial for supporting modern companies in decision-making. This paper introduces a novel data-centric approach called 'forecasting with cross-similarity' to address model uncertainty in a model-free manner. By searching for similar patterns from a reference set, rather than extrapolating, this approach aggregates future paths of similar series to obtain forecasts for the target series. It allows similarity-based forecasting on short series and demonstrates competitive accuracy in real data scenarios.