The estimation of a large covariance matrix is challenging when the dimension p relative to sample size n. Common approaches deal with challenge have been based on thresholding or shrinkage methods in estimating matrices. However, many applications (e.g., regression, forecast combination, portfolio selection), what we need not but its inverse (the precision matrix). In this paper introduce meth...

Time series forecasting has a long track record in many application areas. In forecasting research, it has been illustrated that finding an individual algorithm that works best for all possible scenarios is hopeless. Therefore, instead of striving to design a single superior algorithm, current research efforts have shifted towards gaining a deeper understanding of the reasons a forecasting meth...

In this paper, the time series prediction is as a measure. At the same time, the optimal combination forecast using each method can be defined as the actual impact measurement value of true. Effect of its theoretical estimation has error correlation coefficient values. The optimal weighted linear combination is the theoretical prediction which can be proved, also, simple averaging method is lin...

The combination forecasting model IOWGA-EMD-ARMA-WNN is proposed in this paper. The randomness, periodicity and tendency of the original data are showed by EMD decomposition in EMD-ARMA model. WNN combines the advantages of wavelet analysis and BP neural network and improves the learning efficiency and forecasting accuracy. The weight of combination model is decided by forecasting precision of ...

Considerable literature has accumulated over the years regarding the combination of forecasts. The primary conclusion of this line of research is that forecast accuracy can be substantially improved through the combination of multiple individual forecasts. Furthermore, simple combination methods often work reasonably well relative to more complex combinations. This paper provides a review and a...

in the paper a model to predict the concentrations of particulate matter pm10, pm2.5, so2, no, co and o3 for a chosen number of hours forward is proposed. the method requires historical data for a large number of points in time, particularly weather forecast data, actual weather data and pollution data. the idea is that by matching forecast data with similar forecast data in the historical data...

This study investigates the performance of combination forecasts in comparison to individual forecasts. The empirical study focuses on the UK outbound leisure tourism demand for the USA. The combination forecasts are based on the competing forecasts generated from seven individual forecasting techniques. The three combination methods examined in this study are: the simple average combination me...

Abstract Permafrost and seasonally frozen ground are important surface features in highlatitudes. Because of this, a soil-frost parameterization was added to the Penn State University/NCAR mesoscale meteorological model MM5 in combination with the well validated hydro-thermodynamic soil vegetation scheme HTSVS, which takes into account among other things soil freezing and thawing. Reanalysis of...