Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks

Hybrid models such as the Artificial Neural Network-Autoregressive Integrated Moving Average (ANN–ARIMA) model are widely used in forecasting. However, inaccuracies and inefficiency remain in evidence. To yield the ANN–ARIMA with a higher degree of accuracy, efficiency and precision, the bootstrap and the double bootstrap methods are commonly used as alternative methods through the reconstruction of an ANN–ARIMA standard error. Unfortunately, these methods have not been applied in time series-based forecasting models. The aims of this study are twofold. First, is to propose the hybridization of bootstrap model and that of double bootstrap mode called Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (B-ANN–ARIMA) and Double Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (DB-ANN–ARIMA), respectively. Second, is to investigate the performance of these proposed models by comparing them with ARIMA, ANN and ANN–ARIMA. Our investigation is based on three well-known real datasets, i.e., Wolf's sunspot data, Canadian lynx data and, Malaysia ringgit/United States dollar exchange rate data. Statistical analysis on SSE, MSE, RMSE, MAE, MAPE and VAF is then conducted to verify that the proposed models are better than previous ARIMA, ANN and ANN–ARIMA models. The empirical results show that, compared with ARIMA, ANNs and ANN–ARIMA models, the proposed models generate smaller values of SSE, MSE, RMSE, MAE, MAPE and VAF for both training and testing datasets. In other words, the proposed models are better than those that we compare with. Their forecasting values are closer to the actual values. Thus, we conclude that the proposed models can be used to generate better forecasting values with higher degree of accuracy, efficiency and, precision in forecasting time series results becomes a priority

20-Hydroxyeicosatetraenoic Acid (20-HETE): Bioactions, Receptors, Vascular Function, Cardiometabolic Disease and Beyond

Vascular function is dynamically regulated and dependent on a bevy of cell types and factors that work in concert across the vasculature. The vasoactive eicosanoid, 20-Hydroxyeicosatetraenoic acid (20-HETE) is a key player in this system influencing the sensitivity of the vasculature to constrictor stimuli, regulating endothelial function, and influencing the renin angiotensin system (RAS), as well as being a driver of vascular remodeling independent of blood pressure elevations. Several of these bioactions are accomplished through the ligand-receptor pairing between 20-HETE and its high-affinity receptor, GPR75. This 20-HETE axis is at the root of various vascular pathologies and processes including ischemia induced angiogenesis, arteriogenesis, septic shock, hypertension, atherosclerosis, myocardial infarction and cardiometabolic diseases including diabetes and insulin resistance. Pharmacologically, several preclinical tools have been developed to disrupt the 20-HETE axis including 20-HETE synthesis inhibitors (DDMS and HET0016), synthetic 20-HETE agonist analogues (20-5,14-HEDE and 20-5,14-HEDGE) and 20-HETE receptor blockers (AAA and 20-SOLA). Systemic or cell-specific therapeutic targeting of the 20-HETE-GPR75 axis continues to be an invaluable approach as studies examine the molecular underpinnings activated by 20-HETE under various physiological settings. In particular, the development and characterization of 20-HETE receptor blockers look to be a promising new class of compounds that can provide a considerable benefit to patients suffering from these cardiovascular pathologies