The value of probabilistic forecasting in emergency medical resource planning under uncertainty

PurposeMost epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.Design/methodology/approachTwo probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.FindingsThe managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density; (2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels; and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.Originality/valueVery few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

Transformer for Sub-Seasonal Extreme High Temperature Probabilistic Forecasting Over Eastern China

Sub-seasonal high temperature forecasting is significant for early warning of extreme heat weather. Currently, deep learning methods, especially Transformer, have been successfully applied to the meteorological field. Relying on the excellent global feature extraction capability in natural language processing, Transformer may be useful to improve the ability in extended periods. To explore this, we introduce the Transformer and propose a Transformer-based model, called Transformer to High Temperature (T2T). In the details of the model, we successively discuss the use of the Transformer and the position encoding in T2T to continuously optimize the model structure in an experimental manner. In the dataset, the multi-version data fusion method is proposed to further improve the prediction of the model with reasonable expansion of the dataset. The performance of well-desinged model (T2T) is verified against the European Centre for Medium-Range Weather Forecasts (ECMWF) and Multi-Layer Perceptron (MLP) at each grid of the 100.5°E to 138°E, 21°N to 54°N domain for the April to October of 2016-2019. For case study initiated from 2 June 2018, the results indicated that T2T is significantly better than ECMWF and MLP, with smaller absolute error and more reliable probabilistic forecast for the extreme high event happened during the third week. Over all, the deterministic forecast of T2T is superior to MLP and ECMWF due to ability of utilize spatial information of grids. T2T also provided a better calibrated probability of high temperature and a sharper prediction probability density function than MLP and ECMWF. All in all, T2T can meet the operational requirements for extended period forecasting of extreme high temperature. Furthermore, our research can provide experience on the development of deep learning in this field and achieve the continuous progress of seamless forecasting systems.

Wisdom of the Crowd as Arbiter of Expert Disagreement

How can state-of-the-art probabilistic forecasting tools be used to advance expert debates on big policy questions? Using Foretell, a crowd forecasting platform piloted by CSET, we trialed a method to break down a big question—”What is the future of the DOD-Silicon Valley relationship?”—into measurable components, and then leveraged the wisdom of the crowd to reduce uncertainty and arbitrate disagreement among a group of experts.

Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes

Intermittency are a common and challenging problem in demand forecasting. We introduce a new, unified framework for building probabilistic forecasting models for intermittent demand time series, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but additionally for a natural inclusion of neural network based models—by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling continuous-time demand arrivals, i.e., with a temporal point process, is possible via a trivial extension of our framework. This leads to more flexible modeling in scenarios where data of individual purchase orders are directly available with granular timestamps. Complementing this theoretical advancement, we demonstrate the efficacy of our framework for forecasting practice via an extensive empirical study on standard intermittent demand data sets, in which we report predictive accuracy in a variety of scenarios.