Good Forecasting Ability Research Articles

Predicting the Next Big Thing: Success as a Signal of Poor Judgment

Successfully predicting that something will become a big hit seems impressive. Managers and entrepreneurs who have made successful predictions and have invested money on this basis are promoted, become rich, and may end up on the cover of business magazines. In this paper, we show that an accurate prediction about such an extreme event, e.g., a big hit, may in fact be an indication of poor rather than good forecasting ability. We first demonstrate how this conclusion can be derived from a formal model of forecasting. We then illustrate that the basic result is consistent with data from two lab experiments as well as field data on professional forecasts from the Wall Street Journal Survey of Economic Forecasts.

Study on QSTR of Benzoic Acid Compounds with MCI

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD50 (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD50: 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD50 = 1.2399 × 0JA +2.6911 × 1JA – 0.4445 × JB (R2 = 0.9860), where 0JA is zero order connectivity index, 1JA is the first order connectivity index and JB = 0JA × 1JA is the cross factor. The model was shown to have a good forecasting ability.

Study on the quantitative structure–toxicity relationships of benzoic acid derivatives in rats via oral LD50

Quantitative structure–toxicity relationship (QSTR) studies play an important role in toxicity forecasting, which is widely used in the study of modern compounds. Benzoic acid derivatives are an important type of organic chemicals. Most of them may cause serious public health and environmental problems. The effect of quantum-chemistry parameters on the toxicity of benzoic acid derivatives in rats via oral 50% lethal dose (LD50) was studied by QSTR, and a model to predict the toxicity of benzoic acid derivatives was constructed. In order to obtain an accurate model, cross factors were considered and a model for predicting the toxicity of benzoic acid derivatives in rats via oral LD50 was built using a linear regression method (correlation 0.990). The novel model is $$ - \log^{toxi} = 0.144{\text{Log}}P - 0.0269SAG + 0.0000127HoF - 0.000377PE $$ , R 2 = 0.990, C(p) = 4.000, mean square error (MSE) = 0.785. The model demonstrated good forecasting ability.

Time-Varying Parameters Prediction

This paper develops a method of adaptive modeling that may be applied to forecast non-stationary time series. The starting point are time-varying coefficients models introduced in statistics, econometrics and engineering. The basic step of modeling is represented by the implementation of adaptive recursive estimators for tracking parameters. This is achieved by unifying basic algorithms—such as recursive least squares (RLS) and extended Kalman filter (EKF)—into a general scheme and next by selecting its coefficients with the minimization of the sum of squared prediction errors. This defines a non-linear estimation problem that may be analyzed in the context of the conditional least squares (CLS) theory. A numerical application on the IBM stock price series of Box-Jenkins illustrates the method and shows its good forecasting ability.

Optimized adaptive prediction

This paper develops a method of adaptive modeling that may be applied to forecast nonstationary time series. The starting point are time-varying coefficients models introduced in statistics, econometrics and engineering. The basic step of modeling is represented by the implementation of adaptive recursive estimators for tracking parameters. This is achieved by unifying basic algorithms — such as recursive least squares (RLS) and extended Kalman filter (EKF) — into a general scheme and next by selecting its coefficients with the minimization of the sum of squared prediction errors. This defines a non-linear estimation problem that may be analyzed in the context of the conditional least squares (CLS) theory. Anumerical application on the IBM stock price series of Box-Jenkins illustrates the method and shows its good forecasting ability.

Application of a shape-preserving advection scheme to the moisture equation in an E-grid regional forecast model

This paper presents a methodology which is very useful to design shape-preserving advection finite difference scheme on general E-grid horizontal arrangement of variables through introducing a two-step shape-preserving positive definite advection scheme in the moisture equation of the LASG-REM (LASG regional E-grid eta-coordinate forecast model). By trial-forecasting six local heavy raincases, the efficiency of the shape-preserving advection scheme in practical application has been examined. The LASG-REM with the shape-preserving advection scheme has a good forecasting ability for local precipitation.

Feeder Cattle Forecasting Models: An Econometric Study of Development and Performance

AbstractThe purpose of this paper is to identify whether an intricate forecasting model which takes into consideration selling price, cost of gain, and seasonality will perform better at forecasting quarterly feeder cattle prices than a simpler model based only on the futures price of feeder cattle. Although both models exhibited good forecasting ability, econometric calculations demonstrated that the complex model had more accurate forecasting ability than did the futures model. Both models performed similarly in their ability to predict the directional movement of feeder cattle price.