scholarly journals Seeking Consensus: A New Approach

2010 ◽  
Vol 138 (12) ◽  
pp. 4402-4415 ◽  
Author(s):  
Paul J. Roebber
Keyword(s):  
Minimum Temperature ◽  
Forecast Accuracy ◽  
Cost Savings ◽  
Absolute Error ◽  
Electricity Production ◽  
Forecast Errors ◽  
Consensus Forecasts ◽  
Temperature Forecast ◽  
Model Output Statistics ◽  
The Individual

Abstract Simulated evolution is used to generate consensus forecasts of next-day minimum temperature for a site in Ohio. The evolved forecast algorithm logic is interpretable in terms of physics that might be accounted for by experienced forecasters, but the logic of the individual algorithms that form the consensus is unique. As a result, evolved program consensus forecasts produce substantial increases in forecast accuracy relative to forecast benchmarks such as model output statistics (MOS) and those from the National Weather Service (NWS). The best consensus produces a mean absolute error (MAE) of 2.98°F on an independent test dataset, representing a 27% improvement relative to MOS. These results translate to potential annual cost savings for electricity production in the state of Ohio of the order of $2 million relative to the NWS forecasts. Perfect forecasts provide nearly $6 million in additional annual electricity production cost savings relative to the evolved program consensus. The frequency of outlier events (forecast busts) falls from 24% using NWS to 16% using the evolved program consensus. Information on when busts are most likely can be provided through a logistic regression equation with two variables: forecast wind speed and the deviation of the NWS minimum temperature forecast from persistence. A forecast of a bust is 4 times more likely to be correct than wrong, suggesting some utility in anticipating the most egregious forecast errors. Discussion concerning the probabilistic applications of evolved programs, the application of this technique to other forecast problems, and the relevance of these findings to the future role of human forecasting is provided.

scholarly journals Operational Consensus Forecasts

2005 ◽  
Vol 20 (1) ◽  
pp. 101-111 ◽  
Author(s):  
Frank Woodcock ◽  
Chermelle Engel
Keyword(s):  
Weighted Average ◽  
Ground Level ◽  
Absolute Error ◽  
Model Output ◽  
Consensus Forecasts ◽  
Sunshine Hours ◽  
Operational Forecasting ◽  
Direct Model ◽  
Forecasting System ◽  
Model Output Statistics

Abstract The objective consensus forecasting (OCF) system is an automated operational forecasting system that adapts to underlying numerical model upgrades within 30 days and generally outperforms direct model output (DMO) and model output statistics (MOS) forecasts. It employs routinely available DMO and MOS guidance combined after bias correction using a mean absolute error (MAE)-weighted average algorithm. OCF generates twice-daily forecasts of screen-level temperature maxima and minima, ground-level temperature minima, evaporation, sunshine hours, and rainfall and its probability for day 0 to day 6 for up to 600 Australian sites. Extensive real-time trials of temperature forecasts yielded MAEs at days 0–2 about 40% lower than those from its component MOS and DMO forecasts. MAEs were also lower at day 1 than matching official forecasts of maxima and minima by 8% and 10% and outperformed official forecasts at over 71% and 75% of sites, respectively. MAEs of weighted average consensus outperformed simple average forecasts by about 5%.

Information or Institution?

2011 ◽  
Vol 231 (1) ◽  
Author(s):  
Roland Döhrn ◽  
Christoph M. Schmidt
Keyword(s):  
Personal Experience ◽  
Forecast Accuracy ◽  
Forecast Errors ◽  
Forecast Horizon ◽  
Good Investment ◽  
New Information ◽  
German Research ◽  
Macroeconomic Forecast ◽  
The Individual ◽  
The Impact

SummaryThe accuracy of macroeconomic forecast depends on various factors, most importantly the mix of analytical methods used by the individual forecasters, the way that their personal experience is shaping their identification strategies, but also their efficiency in translating new information into revised forecasts. In this paper we use a broad sample of forecasts of German GDP and its components to analyze the impact of institutions and information on forecast accuracy. We find that forecast errors are a linear function of the forecast horizon, which serves as an indicator of the information available at the time a forecast is produced. This result is robust over a variety of different specifications. As better information seems to be the key to achieving better forecasts, approaches for acquiring reliable information early seem to be a good investment. By contrast, the institutional factors tend to be small and statistically insignificant. It has to remain open, whether this is the consequence of the efficiency-enhancing competition among German research institutions or rather the reflection of an abundance of forecast suppliers.

An Empirical Analysis of Forecast Performance of the GDP Growth in India

2019 ◽  
Vol 20 (2) ◽  
pp. 368-386 ◽  
Author(s):  
Monika Gupta ◽  
Mohammad Haris Minai
Keyword(s):  
Forecast Error ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Gdp Growth ◽  
Forecast Errors ◽  
Forecast Performance ◽  
Reserve Bank ◽  
Rules Of Thumb ◽  
Actual Outcome ◽  
High Degree

This article evaluates the accuracy of a forecast based on the properties of the forecast error. To measure how close the predictions of GDP growth are to the actual outcome in India, we have calculated three measures of forecast accuracy: mean absolute error (MAE), root mean square error (RMSE) and Theil’s U statistic. To evaluate the performance of the forecasts, we have compared them with naive forecast and common rules of thumb, using moving averages (MAs) as rules of thumb. The results are inconclusive regarding biasedness and also inefficient. Further, the forecasts have a high degree of correlation among themselves. The findings of forecast errors suggest that the performance of Reserve Bank of India (RBI) forecasts is favourable compared to other organizations, as well as with respect to the general international standard.

Predicting Earnings Using a Model Based on Cost Variability and Cost Stickiness

2006 ◽  
Vol 81 (2) ◽  
pp. 285-307 ◽  
Author(s):  
Rajiv D. Banker ◽  
Lei (Tony) Chen
Keyword(s):  
Stock Returns ◽  
Forecast Accuracy ◽  
Predictive Ability ◽  
Forecast Model ◽  
Cash Flows ◽  
Substantial Improvement ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Operating Income ◽  
Consensus Forecasts

We evaluate the descriptive validity of the cost behavior model for profit analysis using Compustat data. For this purpose, we propose an earnings forecast model decomposing earnings into components that reflect (1) variability of costs with sales revenue and (2) stickiness in costs with sales declines. We evaluate the predictive ability of our model by benchmarking its performance in forecasting one-year-ahead returns on equity against that of two other time-series models based on line item information reported in the income statement and in the statement of cash flows. Specifically, we consider a model that disaggregates earnings into operating income and non-operating income components and another that disaggregates earnings into cash flows and accruals components. While all three models are less accurate than analysts' consensus forecasts that rely on a larger information set, we find that our model provides substantial improvement in forecast accuracy over the other two models that use only the line items in the financial statements. Finally, invoking the market efficiency assumption, we find that earnings forecast errors based on our model have greater relative information content than forecast errors based on the two alternative models based on financial statement information in explaining abnormal stock returns.

Research on Error Correction and Integration Methods of Maximum and Minimum Temperature Forecast Based on Multi –model in arid area

2020 ◽  
Author(s):  
Jia lihong
Keyword(s):  
Error Correction ◽  
Minimum Temperature ◽  
Average Method ◽  
Arid Area ◽  
Ensemble Average ◽  
Maximum Temperature ◽  
Forecast Errors ◽  
Temperature Prediction ◽  
Temperature Forecast ◽  
Integration Schemes

<p>It is very difficult to predict accurate temperature, especially for maximum and minimum temperature, due to the large diurnal temperature range in arid area. Based on the temperature forecast products from ECMWF, T639, DOGRAFS and GRAPES models and hourly temperature observations at 105 automatic weather stations in Xinjiang during 2013~2015, two kinds of error correction and integration schemes were designed by using the decaying averaging method, ensemble average and weighted ensemble average method, the effects of error correction and integration on predicted maximum and minimum temperature in fore seasons in different partitions Xinjiang were tested contrastively. The first scheme was integrating forecast temperature before correcting errors, while the second scheme was correcting forecast errors firstly and then giving an integration. The results are follows as: (1)The accuracy of temperature predictions from ECMWF model was the best in Xinjiang as a whole, while that from DOGRAFS model was the worst, and the improvement to minimum temperature predictions was higher than that of maximum temperature prediction. (2) With regarding to different partitions Xinjiang, the accuracies of predicted maximum and minimum temperature in northern Xinjiang, west region and plain areas were correspondingly higher than those in southern Xinjiang, east region and mountain areas, and the correction capability to temperature prediction in winter was higher than that in other seasons. (3) The integrated prediction of maximum and minimum temperature by weighted ensemble average method was better than that of ensemble average method. The second scheme was superior to the first scheme. (4) The improvement to maximum(minimum) temperature prediction in the extreme high(low) temperature event process from 13 to 30 July 2017(from 22 to 24 April 2014) in Xinjiang was significant by using the second scheme.</p>

scholarly journals Prediction of the Diurnal Cycle Using a Multimodel Superensemble. Part II: Clouds

2007 ◽  
Vol 135 (12) ◽  
pp. 4097-4116 ◽  
Author(s):  
Arindam Chakraborty ◽  
T. N. Krishnamurti ◽  
C. Gnanaseelan
Keyword(s):  
Diurnal Cycle ◽  
Forecast Accuracy ◽  
Parameterization Scheme ◽  
Forecast Errors ◽  
Single Model ◽  
Past Performance ◽  
Global Spectral Model ◽  
Cloud Models ◽  
Cloud Parameterization ◽  
The Individual

Abstract This study addresses the issue of cloud parameterization in general circulation models utilizing a twofold approach. Four versions of the Florida State University (FSU) global spectral model (GSM) were used, including four different cloud parameterization schemes in order to construct ensemble forecasts of cloud covers. Next, a superensemble approach was used to combine these model forecasts based on their past performance. It was shown that it is possible to substantially reduce the 1–5-day forecast errors of phase and amplitude of the diurnal cycle of clouds from the use of a multimodel superensemble. Further, the statistical information generated in the construction of a superensemble was used to develop a unified cloud parameterization scheme for a single model. This new cloud scheme, when implemented in the FSU GSM, carried a higher forecast accuracy compared to those of the individual cloud schemes and their ensemble mean for the diurnal cycle of cloud cover up to day 5 of the forecasts. This results in a 5–10 W m−2 improvement in the root-mean-square error to the upward longwave and shortwave flux at the top of the atmosphere, especially over deep convective regions. It is shown that while the multimodel superensemble is still the best product in forecasting the diurnal cycle of clouds, a unified cloud parameterization scheme, implemented in a single model, also provides higher forecast accuracy compared to the individual cloud models. Moreover, since this unified scheme is an integral part of the model, the forecast accuracy of the single model improves in terms of radiative fluxes and thus has greater impacts on weather and climate time scales. This new cloud scheme will be tested in real-time simulations.

scholarly journals A Single-Station Approach to Model Output Statistics Temperature Forecast Error Assessment

2005 ◽  
Vol 20 (6) ◽  
pp. 1006-1020 ◽  
Author(s):  
Andrew A. Taylor ◽  
Lance M. Leslie
Keyword(s):  
United States ◽  
The United States ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Forecast Errors ◽  
Model Output ◽  
Error Characteristics ◽  
Temperature Forecast ◽  
Model Output Statistics

Abstract Error characteristics of model output statistics (MOS) temperature forecasts are calculated for over 200 locations around the continental United States. The forecasts are verified on a station-by-station basis for the year 2001. Error measures used include mean algebraic error (bias), mean absolute error (MAE), relative frequency of occurrence of bias and MAE values, and the daily forecast errors themselves. A case study examining the spatial and temporal evolution of MOS errors is also presented. The error characteristics presented here, together with the case study, provide a more detailed evaluation of MOS performance than may be obtained from regionally averaged error statistics. Knowledge concerning locations where MOS forecasts have large errors or biases and why those errors or biases exist is of great value to operational forecasters. Not only does such knowledge help improve their forecasts, but forecaster performance is often compared to MOS predictions. Examples of biases in MOS forecast errors are illustrated by examining two stations in detail. Significant warm and cold biases are found in maximum temperature forecasts for Los Angeles, California (LAX), and minimum temperature forecasts for Las Vegas, Nevada (LAS), respectively. MAE values for MOS temperature predictions calculated in this study suggest that coastal stations tend to have lower MAE values and lower variability in their errors, while forecasts with high MAE and error variability are more frequent in the interior of the United States. Therefore, MAE values from samples of MOS forecasts are directly proportional to the variance in the observations. Additionally, it is found that daily maximum temperature forecast errors exhibit less variability during the summer months than they do over the rest of the year, and that forecasts for any one station rarely follow a consistent temporal pattern for more than two or three consecutive days. These inconsistent error patterns indicate that forecasting temperatures based on recent trends in MOS forecast errors at an individual station is usually not a good strategy. As shown in earlier studies by other authors and demonstrated again here, MOS temperature forecasts are often inaccurate in the vicinity of strong temperature gradients, for locations affected by shallow cold air masses, or for stations in regions of anomalously warm or cold temperatures. Finally, a case study is presented examining the spatial and temporal distributions of MOS temperature forecast errors across the United States from 13 to 15 February 2001. During this period, two surges of cold arctic air moved south into the United States. In contrast to error trends at individual stations, nationwide spatial and temporal patterns of MOS forecast errors could prove to be a powerful forecasting tool. Nationwide plots of errors in MOS forecasts would be useful if made available in real time to operational forecasters.

Rounding of Analyst Forecasts

2005 ◽  
Vol 80 (3) ◽  
pp. 805-823 ◽  
Author(s):  
Don Herrmann ◽  
Wayne B. Thomas
Keyword(s):  
Forecast Accuracy ◽  
Analyst Forecasts ◽  
Forecast Dispersion ◽  
Negative Relation ◽  
Consensus Forecasts ◽  
Earnings Per Share ◽  
Market Expectations

We find that analyst forecasts of earnings per share occur in nickel intervals at a much greater frequency than do actual earnings per share. Analysts who round their earnings per share forecasts to nickel intervals exhibit characteristics of analysts who are less informed, exert less effort, and have fewer resources. Rounded forecasts are less accurate and the negative relation between rounding and forecast accuracy increases as the rounding interval increases from nickel to dime, quarter, half-dollar, and dollar. An examination of announcement period returns reveals that market expectations more closely align with consensus forecasts including rounded forecasts and then correct toward the more accurate consensus forecasts excluding rounded forecasts. Finally, exclusion of rounded forecasts decreases forecast dispersion.

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