Valuable Forecasts Research Articles

Testing the Value of Probability Forecasts for Calibrated Combining

We combine the probability forecasts of a real GDP decline from the U.S. Survey of Professional Forecasters, after trimming the forecasts that do not have “value” as measured by the Kuiper Skill Score and in the sense of Merton (1981). For this purpose, we use a simple test to evaluate the probability forecasts. The proposed test does not require converting the probabilities to binary forecasts before testing, and it accommodates serial correlation and skewness in the forecasts. We find that the number of forecasters making valuable forecasts decreases sharply as horizon increases. The beta-transformed linear pool combination scheme, based on the valuable individual forecasts, is shown to outperform the simple average for all horizons on a number of performance measures including calibration and sharpness. The test helps to identify the good forecasters ex ante, and therefore, contributes to the accuracy of the combined forecasts.

Time-Series Forecast with Adaptive Feedback Controlled Predictor

This paper describes a novel approach to predicting time-series which blends techniques developed in the areas of observer design and numerical solvers for ODEs. The developed predictor is based on a novel feedback control architecture which leads to computationally efficient and a fairly accurate forecast even for volatile economic series. Application to series of various kinds shows that the developed forecaster possesses some basic properties of numerical solvers for ODE. In the same time it prediction horizon is favorably compared with a time step attaining in numerical simulations for the series with precisely known models whereas no knowledge of the series' global model is assumed in our forecast. We demonstrate that for noisy series the accuracy of prediction reduces to the level of noise to signal ratio as well as that reduction of noise by smoothing the series comparably increases the accuracy of prediction. It is also shown that the developed approach provides practically valuable forecast in application to volatile economic series.

A 27 day persistence model of near‐Earth solar wind conditions: A long lead‐time forecast and a benchmark for dynamical models

Geomagnetic activity has long been known to exhibit approximately 27 day periodicity, resulting from solar wind structures repeating each solar rotation. Thus a very simple near‐Earth solar wind forecast is 27 day persistence, wherein the near‐Earth solar wind conditions today are assumed to be identical to those 27 days previously. Effective use of such a persistence model as a forecast tool, however, requires the performance and uncertainty to be fully characterized. The first half of this study determines which solar wind parameters can be reliably forecast by persistence and how the forecast skill varies with the solar cycle. The second half of the study shows how persistence can provide a useful benchmark for more sophisticated forecast schemes, namely physics‐based numerical models. Point‐by‐point assessment methods, such as correlation and mean‐square error, find persistence skill comparable to numerical models during solar minimum, despite the 27 day lead time of persistence forecasts, versus 2–5 days for numerical schemes. At solar maximum, however, the dynamic nature of the corona means 27 day persistence is no longer a good approximation and skill scores suggest persistence is out‐performed by numerical models for almost all solar wind parameters. But point‐by‐point assessment techniques are not always a reliable indicator of usefulness as a forecast tool. An event‐based assessment method, which focusses key solar wind structures, finds persistence to be the most valuable forecast throughout the solar cycle. This reiterates the fact that the means of assessing the “best” forecast model must be specifically tailored to its intended use.

Factors affecting the value of environmental predictions to the energy sector

BackgroundEnergy extraction, production, and transmission systems are highly sensitive to states of the natural environment such as temperature, wind speed, and even ice cover. Forecasts of such state variables are termed environmental predictions. How much value can such environmental predictions provide to the operator of a given energy system? This paper presents three illustrative Canadian case studies, selected to provide a good cross section across sectors, forecast types, and decision time scales, to provide insights into this important question.ResultsUsing these case studies this paper examines what distinguishes economically valuable forecasts from economically less valuable forecasts. It is found that the risk aversion of the decision makers, the degree to which the decision has multiple inputs, and the certainty of the forecasts, together with the sensitivity of the system to the environmental variable in question, all play important roles.ConclusionsTo the extent that risk aversion results from government regulations and organizational guidelines, the conclusions suggest changes in public policy and industry practices that could help unlock value from currently underused forecasts.

Tactical size rotation in Switzerland

Summary The size premium, defined as the return differential between shares of small and large companies, is subject to cyclical fluctuations. This study examines the predictability of this premium for the Swiss stock market applying a new and flexible forecasting approach. Our strategies provide promising information ratios. The results show that risk variables (VIX, TED spread, etc.), the performance of the S&P 500 and statistical variables such as AR(1) terms or trends prove to be successful forecasting variables in our algorithm. Furthermore, variables that sum up the consensus estimates of equity analysts (IBES) make valuable forecast contributions.

LAPS/RAMS. A nonhydrostatic mesoscale numerical modeling system configured for operational use

The National Oceanic and Atmospheric Administration Forecast Systems Laboratory (FSL) has configured an operational local-domain analysis and forecast system designed to function in the local weather office on an inexpensive computer workstation. The real-time system utilizes the 10-km grid increment analyses from the Local Analysis and Prediction System (LAPS) to initialize the Regional Atmospheric Modeling System (RAMS) mesoscale forecast model. A unique aspect of the LAPS/RAMS operational system is the initialization of the high-resolution model with comparable high-resolution analyses. The aim of the LAPS/RAMS real-time system is to provide local-scale guidance to the local weather office in addition to guidance that is currently available from coarser resolution, large-domain operational products. The operational system configuration and the utilization of three-dimensional visualization to display system output is discussed. All components of the LAPS/RAMS system are designed to run efficiently on a computer workstation affordable to individual weather forecast offices. Model validation results suggest that the LAPS/RAMS system is capable of resolving meso-β-scale, often terrain-forced, flow patterns. The three-dimensional visualization of the model output allows the operational forecaster to rapidly peruse the enormous amounts of data and aids the meteorologist's ability to comprehend and conceptualize mesoscale weather events. Potential improvements to the system are being implemented as computer workstation capabilities increase, which should lead to valuable forecast guidance of meso-γ-scale weather phenomena. DOI: 10.1034/j.1600-0870.1995.00125.x

Developing forecasts for better planning

Improvements in accuracy do not necessarily result in more valuable forecasts. Forecasts are of little value, no matter how accurate they are, if they are not used. Many planners distrust forecasts. To help ensure that forecasts are used, forecasters have to determine what types of forecasts are needed. Based on interviews with a dozen veteran forecasters and extensive personal experience, the author develops guidelines for improving the value of forecasts by including planners in the forecast development process. Forecasting reports should be simple and easy to interpret. A preliminary report should be distributed for suggestions to resolve possible problems before the final presentation. After the forecasting numbers are distributed, forecasters should follow up to see how forecasts are used. Such group efforts in preparing forecasts typically improve both the accuracy and the acceptability and thus the value of forecasts.

Probabilistic Short-Term River Yield Forecasts

A predictive model, employing a first order autoregressive structure, which enables estimation of the probability distributions of streamflow volumes in future seasons is developed. The model is applicable to streams that have intraseasonal runoff volumes describable by lognormal probability distributions. A season long observation provides the initial value which is propagated through future seasons by the first order autoregressive streamflow volume structure. For high interseasonal (lag one) correlations application of the model yields valuable forecasts for as many as 10 time periods in the future, while for low correlations such forecasting is of no additional value after four time periods. The method has the greatest utility for streams having high variability and distinct seasonality.