Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms

TL;DR

This paper introduces a fast, scalable forecasting framework for unstable high-frequency demand data streams on on-demand service platforms, demonstrating significant performance improvements and economic benefits across diverse regions and periods.

Contribution

The paper presents a novel forecasting framework that automatically adapts to changing environments and outperforms industry benchmarks in large-scale demand data.

Findings

Strong performance gains over benchmarks across regions and periods

Economic benefits include financial gains and reduced computing costs

Effective in both pre- and post-Covid demand scenarios

Abstract

On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable, and automatically assesses changing environments without human intervention. We empirically test our framework on a large-scale demand data set from a leading on-demand delivery platform in Europe, and find strong performance gains from using our framework against several industry benchmarks, across all geographical regions, loss functions, and both pre- and post-Covid periods. We translate forecast gains to economic impacts for this on-demand service platform by computing financial gains and reductions in computing costs.