A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information

Time series forecasting represents a significant and challenging task across various fields. Recently, methods based on mode decomposition have dominated the forecasting of complex time series because of the advantages of capturing local characteristics and extracting intrinsic modes from data. Unfortunately, most models fail to capture the implied volatilities that contain significant information. To enhance the prediction of contemporary diverse and complex time series, we propose a novel time series forecasting paradigm that integrates decomposition with the capability to capture the underlying fluctuation information of the series. In our methodology, we implement the Variational Mode Decomposition algorithm to decompose the time series into K distinct sub-modes. Following this decomposition, we apply the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to extract the volatility information in these sub-modes. Subsequently, both the numerical data and the volatility information for each sub-mode are harnessed to train a neural network. This network is adept at predicting the information of the sub-modes, and we aggregate the predictions of all sub-modes to generate the final output. By integrating econometric and artificial intelligence methods, and taking into account both the numerical and volatility information of the time series, our proposed framework demonstrates superior performance in time series forecasting, as evidenced by the significant decrease in MSE, RMSE, and MAPE in our comparative experimental results

ATXN7 Gene Variants and Expression Predict Post-Operative Clinical Outcomes in Hepatitis B Virus-Related Hepatocellular Carcinoma

Background/Aims: Hepatocellular carcinoma (HCC) is a lethal disease with nearly equal morbidity and mortality. Thus, the discovery and application of more useful predictive biomarkers for improving therapeutic effects and prediction of clinical outcomes is of crucial significance. Methods: A total of 475 HBV-related HCC patients were enrolled. Ataxin 7 (ATXN7) single nucleotide polymorphisms (SNPs) were genotyped by Sanger DNA sequencing after PCR amplification. The associations between ATXN7 SNPs and mRNA expression with the prognosis of HBV-related HCC were analyzed. Results: In all, rs3774729 was significantly associated with overall survival (OS) of HBV-related HCC (P = 0.013, HR = 0.66, 95% CI: 0.48-0.94). And patients with the AA genotype and a high level of serum alpha fetoprotein (AFP) had significantly worse OS when compared to patients with AG/GG genotypes and a low level of AFP (adjusted P = 0.007, adjusted HR = 1.83, 95% CI = 1.18-2.82). Furthermore, low expression of ATXN7 was significantly associated with poor recurrence-free survival (RFS) and OS (P = 0.007, HR = 2.38, 95% CI = 1.27-4.45 and P = 0.025, HR = 1.75, 95% CI = 1.18-2.62). Conclusion: ATXN7 may be a potential predictor of post-operative prognosis of HBV-related HCC