Digital currency price prediction is vital to both sellers and purchasers. Over these years, decomposition and integration models have been applied more and more to realize the goal of precise prediction, however, many of them tend to neglect the reconstruction of features or the residual series. Altogether, one of the biggest drawbacks of the decomposition and integration framework is the method applied requires manual parameter setting whether it is for decomposition or integration. Still, for the results, they are merely satisfied with the point prediction which brings high uncertainty. In this paper, an optimized feature reconstruction decomposition and two-step nonlinear integration method is proposed which gives consideration to feature reconstruction, nonlinear integration, optimization and interval prediction. The original data series is decomposed through improved variational mode decomposition based approximate entropy feature reconstruction system. Then, improved particle swarm optimization-gated recurrent unit (iPSO-GRU) is utilized in the first and second nonlinear integration part separately. Meanwhile, the residual series is given attention, if it is not a white noise series, the residual will be the input of iPSO-GRU whose result will be added back to the second integration result to form the point prediction result. Based on the point prediction result, interval prediction estimate will be generated as well via maximum likelihood function. This study chooses three kinds of digital currency as cases and the results show that the MAPE values of point prediction are all below 3.5%, and CP values of interval prediction are all 1 with suitable MWP. In addition, compared with other benchmark models, the proposed model shows better performance.
MgAl2O4 transparent ceramics were shaped by a commonly used polyacrylic acid (PAA), which acted as both dispersant and gelling agent. The spinel slurries were prepared by ball-milling MgAl2O4 powder, PAA, and water in an attrition mill. The gelling of slurries happened at room temperature in air atmosphere without any other organic additive. The gelling mechanism was the formation of chelates between Mg2+ and carboxyl groups (-COO−) of PAA. The frequency-based testing method was applied to investigate the gelling process of the as-prepared slurry. In addition, a novel in situ characterization method based on a modified indentation testing was invented to better understand the strengthening of the wet green body with time and to guide when demolding could be carried out. After sintering, transparent MgAl2O4 ceramics with high in-line transmittance were resulted. 相似文献
Magnetic Resonance Materials in Physics, Biology and Medicine - To evaluate the placental function by monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in patients with... 相似文献
Conversion of LCO (light cycle oil) to BTX (benzene, toluene, and xylene) is an economically valuable method for refineries. However, this approach still faces difficulties as the main reactions are not clearly understood. Here we study the detailed hydrocracking pathway of typical reactants, 1-methylnaphthalene and tetralin, through molecular simulations and experiments to improve our understanding of the conversion process of LCO to BTX. Molecular simulations demonstrate that the rate-determining step is the isomerization pathway of six-membered ring to five-membered ring in tetralin as its activation energy (ΔEa) is the highest among all the reactions and the order of ΔEa of reactions is isomerization > ring-opening ≈ side-chain cleavage. The results of experiments show that with the increase in reaction depth, i.e., through a high temperature (350 – 370 °C) and low LHSV (4.5 – 6.0 h−1), isomerization, ring-opening, and side-chain cleavage reactions occurred, thus improving the selectivity and yield of alkyl aromatics. 相似文献
