On Forecast Stability
Rakshitha Godahewa Christoph Bergmeir Zeynep Erkin Baz Chengjun Zhu Zhangdi Song Salvador García Dario Benavides
Forecasts are typically not produced in a vacuum but in a business context,
where forecasts are generated on a regular basis and interact with each other.
For decisions, it may be important that forecasts do not change arbitrarily,
and are stable in some sense. However, this area has received only limited
attention in the forecasting literature. In this paper, we explore two types of
forecast stability that we call vertical stability and horizontal stability.
The existing works in the literature are only applicable to certain base models
and extending these frameworks to be compatible with any base model is not
straightforward. Furthermore, these frameworks can only stabilise the forecasts
vertically. To fill this gap, we propose a simple linear-interpolation-based
approach that is applicable to stabilise the forecasts provided by any base
model vertically and horizontally. The approach can produce both accurate and
stable forecasts. Using N-BEATS, Pooled Regression and LightGBM as the base
models, in our evaluation on four publicly available datasets, the proposed
framework is able to achieve significantly higher stability and/or accuracy
compared to a set of benchmarks including a state-of-the-art forecast
stabilisation method across three error metrics and six stability metrics.