Forecast selection and combination are regarded as two competing alternatives. In the literature there is substantial evidence that forecast combination is beneficial, in terms of reducing the ...forecast errors, as well as mitigating modelling uncertainty as we are not forced to choose a single model. However, whether all forecasts to be combined are appropriate, or not, is typically overlooked and various weighting schemes have been proposed to lessen the impact of inappropriate forecasts. We argue that selecting a reasonable pool of forecasts is fundamental in the modelling process and in this context both forecast selection and combination can be seen as two extreme pools of forecasts. We evaluate forecast pooling approaches and find them beneficial in terms of forecast accuracy. We propose a heuristic to automatically identify forecast pools, irrespective of their source or the performance criteria, and demonstrate that in various conditions it performs at least as good as alternative pools that require additional modelling decisions and better than selection or combination.
•Forecast selection and combination assume an existing set of reasonable forecasts.•There is limited work how to construct these pools of forecasts, albeit important.•We argue that forecast selection and combination are extremes of the pooling spectrum.•We propose a model- and criterion-independent approach to construct forecast pools.•The proposed `forecast islands' improve performance and reduce computational effort.
Forecast combinations: An over 50-year review Wang, Xiaoqian; Hyndman, Rob J.; Li, Feng ...
International journal of forecasting,
October-December 2023, 2023-10-00, Letnik:
39, Številka:
4
Journal Article
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Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of mainstream forecasting research and activities. Combining multiple forecasts ...produced for a target time series is now widely used to improve accuracy through the integration of information gleaned from different sources, thereby avoiding the need to identify a single “best” forecast. Combination schemes have evolved from simple combination methods without estimation to sophisticated techniques involving time-varying weights, nonlinear combinations, correlations among components, and cross-learning. They include combining point forecasts and combining probabilistic forecasts. This paper provides an up-to-date review of the extensive literature on forecast combinations and a reference to available open-source software implementations. We discuss the potential and limitations of various methods and highlight how these ideas have developed over time. Some crucial issues concerning the utility of forecast combinations are also surveyed. Finally, we conclude with current research gaps and potential insights for future research.
It is commonly accepted that information is helpful if it can be exploited to improve a decision making process. In economics, decisions are often based on forecasts of the upward or downward ...movements of the variable of interest. We point out that directional forecasts can provide a useful framework for assessing the economic forecast value when loss functions (or success measures) are properly formulated to account for the realized signs and realized magnitudes of directional movements. We discuss a general approach to (directional) forecast evaluation which is based on the loss function proposed by Granger, Pesaran and Skouras. It is simple to implement and provides an economically interpretable loss/success functional framework. We show that, in addition, this loss function is more robust to outlying forecasts than traditional loss functions. As such, the measure of the directional forecast value is a readily available complement to the commonly used squared error loss criterion.
The use of a conditionally unbiased, but imperfect, volatility proxy can lead to undesirable outcomes in standard methods for comparing conditional variance forecasts. We motivate our study with ...analytical results on the distortions caused by some widely used loss functions, when used with standard volatility proxies such as squared returns, the intra-daily range or realised volatility. We then derive necessary and sufficient conditions on the functional form of the loss function for the ranking of competing volatility forecasts to be robust to the presence of noise in the volatility proxy, and derive some useful special cases of this class of “robust” loss functions. The methods are illustrated with an application to the volatility of returns on IBM over the period 1993 to 2003.
Near‐term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has ...been no cross‐ecosystem analysis of near‐term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near‐term (≤10‐yr forecast horizon) ecological forecasting papers to understand the development and current state of near‐term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near‐term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near‐term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.
•Spatially and temporally adjusted GCM ensembles for regional crop yield forecasts.•ENSO-analogue and GCM-derived systems produce reliable wheat yield forecasts.•GCM-derived wheat forecasts ...demonstrate overall slightly improved skill.•GCM-derived forecasts show enhanced lead time and improved skill before planting.
Foresight of crop yield is fundamental to producers and industry to better manage climate risks and mitigate ebbs and troughs in crop production. Rain-fed grain production in Australia is highly volatile and producers and industry are progressively confronted with projected uncertainties due to climate variability and change, input costs and market prices. Thus, having advance knowledge of the likely impact of the coming season's climate on crop yield and production is critical for decisions across the supply chain. Here we explore and analyse the lead time and skill of a wheat yield forecasting system using a biophysical crop yield simulation model connected to either a statistical ENSO-analogue climate forecasting system or a dynamic general circulation model (GCM) derived climate forecasting system. The comparative skill was investigated for 16 wheat producing districts (shires) of the broad Australian winter cropping region, each containing 9–35 irregularly-spaced simulation points associated with climate stations. Both the ENSO-analogue and GCM-derived systems produced reliable wheat yield forecasts with the GCM-based approach having general improved skill, and particularly during the early months of the season (March to May) before sowing. The shift in the forecast yield distributions relative to the climatology-based yield distribution were dependent on location and time in the season, with the GCM-derived forecast shifts more widespread and earlier in the season. Overall, the GCM-based climate/crop forecasting system showed a significant improvement in lead time (greater than two months before the normal planting time of wheat), across the Australian wheat belt. This result demonstrates an avenue for improved efficacy in future commodity forecasting frameworks via likely enhanced relevance and utility to industry associated with the use of GCM-derived approaches.
In this paper, we build upon a recently proposed forecast combination-based approach to the reconciliation of a simple hierarchy (Hollyman R., Petropoulos F., Tipping M.E., Understanding forecast ...reconciliation, European Journal of Operational Research, 2021, 294, 149–160) and extend it in some new directions. In particular, we provide insights into the nature and mathematical derivation of the level-l conditional coherent (LlCC) point forecast reconciliation procedure for an elementary two-level hierarchy. We show that: (i) the LlCC procedure is the result of a linearly constrained minimization of a quadratic loss function, with an exogenous constraint given by the base forecast of the top level series of the hierarchy, which is not revised; and (ii) endogenous constraints may also be considered in the same framework, thereby resulting in level conditional reconciled forecasts where both the top and the bottom level time series forecasts are coherently revised. In addition, we show that the LlCC procedure (i.e., with exogenous constraints but the result also holds in the endogenous case) does not guarantee the non-negativity of the reconciled forecasts, which can be an issue in cases when non-negativity is a natural attribute of the variables that need to be forecast (e.g., sales and tourism flows). Finally, we consider two forecasting experiments to evaluate the performance of various cross-sectional forecast combination-based point forecast reconciliation procedures (vis-à-vis the state-of-the-art procedures) in a fair setting. In this framework, due to the crucial role played by the (possibly different) models used to compute the base forecasts, we re-interpret the combined conditional coherent reconciliation procedure (CCCH) of Hollyman et al. (2021) as a forecast pooling approach, and show that accuracy improvements may be obtained by adopting a simple forecast averaging strategy.
In this report, we analyze historical and forecast infections for COVID-19 death based on Reduced-Space Gaussian Process Regression associated to chaotic Dynamical Systems with information obtained ...in 82 days with continuous learning, day by day, from January 21th, 2020 to April 12th. According last results, COVID-19 could be predicted with Gaussian models mean-field models can be meaning- fully used to gather a quantitative picture of the epidemic spreading, with infections, fatality and recovery rate. The forecast places the peak in USA around July 14th 2020, with a peak number of 132,074 death with infected individuals of about 1,157,796 and a number of deaths at the end of the epidemics of about 132,800. Late on January, USA confirmed the first patient with COVID-19, who had recently traveled to China, however, an evaluation of states in USA have demonstrated a fatality rate in China (4%) is lower than New York (4.56%), but lower than Michigan (5.69%). Mean estimates and uncertainty bounds for both USA and his cities and other provinces have increased in the last three months, with focus on New York, New Jersey, Michigan, California, Massachusetts, ... (January e April 12th). Besides, we propose a Reduced-Space Gaussian Process Regression model predicts that the epidemic will reach saturation in USA on July 2020. Our findings suggest, new quarantine actions with more restrictions for containment strategies implemented in USA could be successfully, but in a late period, it could generate critical rate infections and death for the next 2 month.
“Iterated” multiperiod-ahead time series forecasts are made using a one-period ahead model, iterated forward for the desired number of periods, whereas “direct” forecasts are made using a ...horizon-specific estimated model, where the dependent variable is the multiperiod ahead value being forecasted. Which approach is better is an empirical matter: in theory, iterated forecasts are more efficient if the one-period ahead model is correctly specified, but direct forecasts are more robust to model misspecification. This paper compares empirical iterated and direct forecasts from linear univariate and bivariate models by applying simulated out-of-sample methods to 170 U.S. monthly macroeconomic time series spanning 1959–2002. The iterated forecasts typically outperform the direct forecasts, particularly, if the models can select long-lag specifications. The relative performance of the iterated forecasts improves with the forecast horizon.
National Center for Environmental Prediction (NCEP) started distributing global operational gridded in flight icing, turbulence and convective cloud products as part of World Area Forecast System ...(WAFS) products in 2007. Simple algorithms were used to derive these products during early stage based on NCEP Global Forecast System (GFS) forecast. These products quickly became essential flight planning tool for international aviation community and are especially important to developing countries that do not have resource to run numerical models themselves. To further improve these products, Environmental Modeling Center (EMC) started collaborating with National Center for Atmospheric Research (NCAR) to transition their aviation research algorithms into NCEP’s operations (R2O), particularly Forecast Icing Potential (FIP) and Graphical Turbulence Guidance (GTG) algorithms. The initial attempt is to apply FIP to GFS forecast to potentially replace WAFS icing product. Extensive evaluation demonstrated FIP outperformed original WAFS icing product and, with support from Aviation Weather Center (AWC) and Federal Aviation Administration (FAA), EMC replaced US WAFS icing product with FIP in 2015. EMC recently also implemented GTG with 2017 GFS upgrade but GTG will not replace WAFS turbulence until 2019. This paper will describe the methodology which EMC used to transition NCAR’s aviation research algorithms into NCEP’s operations. It will also describe how EMC generates icing analysis data to be used as truth for performing objective verification. Several case studies will be presented and the methodology and results for objective validation will be discussed. Finally, future collaboration plan with NCAR and implementation plans to continue to improve WAFS products will be stated.
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