Hybrid grey model to forecast monitoring series with seasonality

Abstract

The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM (1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i. e. , seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean absolute error and mean square error of seasonal index-GM(1,1) are 30.69 M and 54.53 % smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.

Prediction for the Sluice Deformation Based on SOA-LSTM-Weighted Markov Model

“…For example, mathematical statistics, structural analysis and artificial intelligence algorithms have been utilized in the studies of variation law, early warning and risk analysis related to the deformation of dams for decades [18][19][20]. Recently, with the rapid development of artificial intelligence algorithms, artificial neural networks [21][22][23], grey system models [24][25][26], clustering algorithms [27][28][29] and intelligent optimization algorithms [30][31][32] have been widely applied in the deformation prediction of hydraulic structure engineering. These algorithms are able to overcome the shortcomings of traditional prediction models in terms of multidimensional input, model adaptive learning and overfitting.…”

Interfacial Thermal Resistance in Magnetocaloric Epoxy‐Bonded La‐Fe‐Co‐Si Composites

“…In fact, interfacial thermal resistance might even be the limiting factorf or thermal transport in these composites.N evertheless,t he value of interfacialt hermalr esistancem ight depend on the material combination as well as the size and shape of the particles. [16,17] Thev alue of the interfacial thermal resistance in composites is typically in the range of 10 À9 to 10 À4 Km 2 W À1 . [12,14,17] Thev alue of R i = 0.010 AE 0.005 Km 2 W À1 reported here is much higher than these typicalv alues,w hich may indicate low adhesion of the polymerm atrixt ot he surface.A nother factor contributing towards the increased interfacial thermal resistance that we observe could be the pores that are present in the polymer but that are not included in the simulation because they cannot be sufficiently distinguished from the matrix.H owever, more detailed measurements and simulations are needed to examine the true origin of this large interfacial resistance.…”

“…[16,17] Thev alue of the interfacial thermal resistance in composites is typically in the range of 10 À9 to 10 À4 Km 2 W À1 . [12,14,17] Thev alue of R i = 0.010 AE 0.005 Km 2 W À1 reported here is much higher than these typicalv alues,w hich may indicate low adhesion of the polymerm atrixt ot he surface.A nother factor contributing towards the increased interfacial thermal resistance that we observe could be the pores that are present in the polymer but that are not included in the simulation because they cannot be sufficiently distinguished from the matrix.H owever, more detailed measurements and simulations are needed to examine the true origin of this large interfacial resistance. In general, the results show that matrixm aterials for magnetocaloric composites should not only be compared regarding their thermal conductivitya nd mechanical stability,b ut also regarding their interfacial resistance for heat transport to the magnetocaloric material.…”

Characterization of the Mixing Flow Structure of Molten Steel in a Single Snorkel Vacuum Refining Furnace

“…Starting from the 1990s, numbers of research works have been devoted to investigate the performance and characteristics of the SSF. A number of hydrodynamic models have been constructed to study the metallurgical characteristics of the SSF [2][3][4][5][6][7]. Cheng et al [2,3] studied the kinetics of deoxidization and the slag entrapment behavior of SSF using the water model experiment, and the expression of deoxidization rate and gas blowing flow rate of critical slag entrapment have been obtained.…”

“…Qin et al [5] studied the effect of alloy addition method on the mixing time of SSF with the water model experiment. Zhao and Wang et al [6,7] studied the kinetics of decarburization of SSF using the water model experiment and gave the expression of deoxidization rate. Furthermore, some plant testing of SSF have been done by some researchers in China.…”