Continuous glucose monitoring(CGM)is a popular technology among the diabetic population,especially in patients with type 1 diabetes and those with type 2 diabetes treated with insulin.The American Diabetes Association...Continuous glucose monitoring(CGM)is a popular technology among the diabetic population,especially in patients with type 1 diabetes and those with type 2 diabetes treated with insulin.The American Diabetes Association recommends standardization of CGM reports with visual cues,such as the ambulatory glucose profile.Nevertheless,interpreting this report requires training and time for CGM to be cost-efficient.In this work it has been proposed to incorporate Japanese candlestick charts in glucose monitoring.These graphs are used in price analysis in financial markets and are easier to view.Each candle provides extra information to make prudent decisions since it reports the opening,maximum,minimum and closing glucose levels of the chosen time frame,usually the daily one.The Japanese candlestick chart is an interesting tool to be considered in glucose control.This graphic representation allows identification of glucose trends easily through the colors of the candles and maximum and minimum glucose values.展开更多
The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial practice.However,the inherent constraints in OHLC data pose immense challenges to its structural ...The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial practice.However,the inherent constraints in OHLC data pose immense challenges to its structural modeling.Models that fail to process these constraints may yield results deviating from those of the original OHLC data structure.To address this issue,a novel unconstrained transformation method,along with its explicit inverse transformation,is proposed to properly handle the inherent constraints of OHLC data.A flexible and effective framework for structurally modeling OHLC data is designed,and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series analysis.Extensive simulations and three authentic financial datasets from the Kweichow Moutai,CSI 100 index,and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling approach.The modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.展开更多
文摘Continuous glucose monitoring(CGM)is a popular technology among the diabetic population,especially in patients with type 1 diabetes and those with type 2 diabetes treated with insulin.The American Diabetes Association recommends standardization of CGM reports with visual cues,such as the ambulatory glucose profile.Nevertheless,interpreting this report requires training and time for CGM to be cost-efficient.In this work it has been proposed to incorporate Japanese candlestick charts in glucose monitoring.These graphs are used in price analysis in financial markets and are easier to view.Each candle provides extra information to make prudent decisions since it reports the opening,maximum,minimum and closing glucose levels of the chosen time frame,usually the daily one.The Japanese candlestick chart is an interesting tool to be considered in glucose control.This graphic representation allows identification of glucose trends easily through the colors of the candles and maximum and minimum glucose values.
基金the financial support from the Beijing Natural Science Foundation(Grant No.9244030)the National Natural Science Foundation of China(Grant Nos.72021001,11701023).
文摘The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial practice.However,the inherent constraints in OHLC data pose immense challenges to its structural modeling.Models that fail to process these constraints may yield results deviating from those of the original OHLC data structure.To address this issue,a novel unconstrained transformation method,along with its explicit inverse transformation,is proposed to properly handle the inherent constraints of OHLC data.A flexible and effective framework for structurally modeling OHLC data is designed,and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series analysis.Extensive simulations and three authentic financial datasets from the Kweichow Moutai,CSI 100 index,and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling approach.The modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.
