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on Forecasting |
| By: | Bårdsen, Gunnar (NTNU); Nymoen, Ragnar (Dept. of Economics, University of Oslo) |
| Abstract: | A framework for forecasting new COViD-19 cases jointly with hospital admissions and hospital beds with COVID-19 cases is presented. This project, dubbed CovidMod, produced 21-days ahead forecasts each working day from March 2021 to April 2022, and forecast errors that were used to assess forecast accuracy. A comparison with the forecasts of the Norwegian Institute of Public Health (NIPH), with dates of origin in the same period, favours the CovidMod forecasts in terms of lower RMSFEs (Root Mean Squared Forecast Errors), both for new cases and for hospital beds. Another comparison, with the short term forecasts (7 day horizon) produced by a forecasting project at the University of Oxford, shows only little difference in terms of the RMSFEs of new cases. Next, we present a further development of the model which allows the effects of policy responses to a central model parameter to be forecasted by an estimated smooth-transition function. The forecasting performance of the resulting non-linear model is demonstrated, and it is suggested as a possible way forward in the development of relevant forecasting tools in general and for pandemics in particular. |
| Keywords: | C32; C53; C54 |
| JEL: | C32 C53 C54 |
| Date: | 2023–05–23 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:osloec:2023_003&r=for |
| By: | M. Hashem Pesaran; Ron P. Smith |
| Abstract: | Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1-2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables. |
| Keywords: | forecasting, high-dimensional data, Lasso, OCMT, latent factors, principal components |
| JEL: | C53 C55 E37 E52 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10931&r=for |
| By: | Oscar Trull; J. Carlos Garc\'ia-D\'iaz; Angel Peir\'o-Signes |
| Abstract: | Transmission system operators have a growing need for more accurate forecasting of electricity demand. Current electricity systems largely require demand forecasting so that the electricity market establishes electricity prices as well as the programming of production units. The companies that are part of the electrical system use exclusive software to obtain predictions, based on the use of time series and prediction tools, whether statistical or artificial intelligence. However, the most common form of prediction is based on hybrid models that use both technologies. In any case, it is software with a complicated structure, with a large number of associated variables and that requires a high computational load to make predictions. The predictions they can offer are not much better than those that simple models can offer. In this paper we present a MATLAB toolbox created for the prediction of electrical demand. The toolbox implements multiple seasonal Holt-Winters exponential smoothing models and neural network models. The models used include the use of discrete interval mobile seasonalities (DIMS) to improve forecasting on special days. Additionally, the results of its application in various electrical systems in Europe are shown, where the results obtained can be seen. The use of this library opens a new avenue of research for the use of models with discrete and complex seasonalities in other fields of application. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.10982&r=for |
| By: | Jeroen Rombouts; Marie Ternes; Ines Wilms |
| Abstract: | Platform businesses operate on a digital core and their decision making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all levels of the hierarchy to ensure aligned decision making across different planning units such as pricing, product, controlling and strategy. Given that platform data streams feature complex characteristics and interdependencies, we introduce a non-linear hierarchical forecast reconciliation method that produces cross-temporal reconciled forecasts in a direct and automated way through the use of popular machine learning methods. The method is sufficiently fast to allow forecast-based high-frequency decision making that platforms require. We empirically test our framework on a unique, large-scale streaming dataset from a leading on-demand delivery platform in Europe. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.09033&r=for |