Regression and forecasting performance
Macroeconomic forecasting using penalized regression methods
... impressive forecasting performance of factor models on macroeconomic datasets from the ...-penalized regression has gained more appeal and the body of applied literature taking into account these ...
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Macroeconomic Forecasting Using Penalized Regression Methods
... ridge regression can be viewed as a smoothed version of principal components and De Mol et ...the forecasting performance; under collinearity a few appropriately selected variables could capture the ...
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Inflation and unemployment forecasting with genetic support vector regression
... Vector Regression (GA-SVR) model in economic forecasting and macroeconomic variable selection is ...of forecasting the US inflation and ...The forecasting performance of the GA-SVR is ...
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Tail-Heaviness, Asymmetry, and Profitability Forecasting by Quantile Regression
... a forecasting approach under a given evaluation ...quantile regression forecasts varies with different measures of asymmetry, however, in a less consistent ...our forecasting accuracy measures, ...
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Macroeconomic Forecasting with Factor-Augmented Adjusted Band Regression
... 3.2. Forecasting the Major Measures of Economic Activity In a rolling analysis, each subsample of n = 100 successive quarters is used to partial out the autoregressive dynamics (up to lag four), estimate the ...
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Testing for changes in forecasting performance
... the forecasting regression, via the conditional mean, or from changes in the variance of the ...the forecasting model is misspeci…ed in which case an over-…tting problem is possible, so that the ...
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Least angle regression for time series forecasting with many predictors.
... A distinct feature of TS–LARS is that it allows for a ranking of the different predictors. This ranking differs according to the series one wants to predict and the forecast horizon. The highest ranked time series are ...
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The evaluation of it specialists’ performance based on the grading system and it’s forecasting by means of regression models
... the performance of IT specialists by the grading system using regression analysis, where the predictors are the individual potentials of IT ...for forecasting the professional performance of ...
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A COMPARISON OF REGRESSION MODELS FOR FORECASTING A CUMULATIVE VARIABLE
... relative performance of four regression models for forecasting total demand when historical time series data for past sales and partial demand data for future orders are ...(OLS) regression ...
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Distributional regression for demand forecasting in e-grocery
... distributional regression methods — so-called Generalised Additive Models for Location, Scale and Shape (GAMLSS) — to arrive at the cost-minimising solution according to the newsvendor ...linear regression, ...
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Time series forecasting by principal covariate regression.
... the performance of PCovR if the structure parameters of the forecast model are not fixed a priori but are selected by using the Bayes information criterion (BIC) or cross validation ...
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Support vector regression for warranty claim forecasting
... to forecasting warranty claims: the first is a weighted support vector re- gression (SVR) model and the second is a weighted SVR-based time series ...ahead forecasting, the results show that the proposed ...
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Daily Discharge Forecasting Using Least Square Support Vector Regression and Regression Tree
... good performance in predicting peak discharges, because peak discharges have low participation in the training model, and the major portion of the training data includes base ow or values close to ...
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Forecasting Grain Supply and Demand with Support Vector Regression
... Abstract. The paper uses the machine leaning algorithm to analyses grain supply and demand of China. For the sake of small samples, support vector regression is used to forecast the tread of grain supply and ...
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Short-term Time Series Forecasting with Regression Automata
... While it is generated much faster, it does not generalize, and is large in size. In Figure 6, shows the training and testing error in prefix trees with different depths. The longer the window size, i.e. the higher the ...
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Support Vector Regression In Statistical Downscaling For Rainfall Forecasting
... The new variable from reduction results are used to build the SVR model. Kernel function is used to obtain nonlinear problem in the SVR model. RBF kernel function was chosen because it has a high sensitivity on model ...
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A Regression Approach for Forecasting Vendor Revenue in Telecommunication Industries
... Keyword- Linear Regression, Sequential Minimal Optimization Algorithm, M5rules I. I NTRODUCTION In telecommunication industries, companies have to strategize in developing successful contractors because they ...
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GPR Regression for Tourism Demand Forecasting in Hong Kong
... GP regression (GPR) model for tourism demand forecasting in Hong ...the performance of the sparse GPR model with those of various kernel- based models to show its ...its forecasting capa- ...
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Sales Forecasting using Linear Regression and K-Nearest Neighbour
... ABSTRACT: Forecasting sales is a typical undertaking performed by ...Machine Forecasting Duel - allow us daily with your information and we'll give a calculation based fair- minded gauge or weight and ...
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Distributed nonparametric and semiparametric regression on SPARK for big data forecasting
... RDDs [21] are distributed memory abstractions that allow programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce. RDDs are motivated by two ...
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