A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Comparison of a five-year clinical outcome between Chinese women and men with de novo coronary disease treated with implantation of a drug-eluting stent： a three-center, prospective, registry study

Background The gender-based differences in adverse events after drug-eluting stent （DES） implantation between Chinese women and men have not been fully studied. The present study aimed to compare the 5-year clinical outcome after DES implantation in Chinese women and men. Methods Chinese women （n=298） and men （n=698） with newly diagnosed de novo coronary lesions were studied after DES implantation. The primary endpoint was the occurrence of major adverse cardiac events （MACEs） over a 5-year follow-up, including myocardial infarction （MI）, cardiac death, and target vessel revascularization （TVR）. Propensity score matching （PSM） was used to compare the adjusted MACE rates between sexes. Results Women differed in body habitus and had increased fasting cholesterol. Fewer women presented with MI, and they had better cardiac function with less complex disease. The unadjusted rate of MI at 3 years （2.1%） and 5 years （5.0%） and MACE （25.2%） at 5 years in men was significantly higher than that of women （0.3%, 1.0% and 17.8%, P=0.050, P=0.032, and P=0.011, respectively）. After PSM, the adjusted adverse events between sexes were similar. The stent thrombosis rate rapidly increased after 2 years in men. Conclusions There were significant gender-based differences in baseline characteristics. Chinese men had equivalent outcomes to women after DES after adjustment by PSM. The increased rate of MI in men was attributed to an increased unadjusted rate of MACE.