Forecasting with Temporal Hierarchies

Forecasts are essential for decisions. Decision makers need both forecasts and an understanding of their associated uncertainties. Uncertain forecasts result in more expensive decisions, as one needs to plan for the additional risk.

For example, if forecasts are used for inventory decisions, then more accurate forecasts require less safety stock to cover the uncertain demand. This reduces the capital tied to stock and reduces potential waste due to goods perishing or becoming obsolete. This has motivated extensive research in forecasting.

Temporal hierarchies is a new approach to forecasting that is very competitive with existing methods. Its key characteristic is that it models data from multiple perspectives and then combines them into a single holistic model. These different perspectives are produced by temporally aggregating the data, for instance from daily to weekly. This helps reveal additional information that can be modelled.

This research improves on temporal hierarchies. First, it looks at the estimation of forecast uncertainty. Currently, it cannot provide this, limiting its applications.

Second, it investigates the impact of improved forecasts on the decision metrics, like reduction of waste, rather than proxies such as forecast accuracy. Although there is an expectation that accurate forecasts lead to better decisions, in practice many other factors influence this. Revealing these can help the design of better forecasts and decisions.