「 A Prediction Approach for Stock Market Volatility Based on Time Series Data 」 ieee access 改写 intro

Stock market is full of uncertainly factor , and stock price is fluctuate wave that impossible to control . However , trying to predict stock price is a vital act on business domain , every investor want to analysis the stock and forecast stock price , although that the dangerous of lose all investment risk is exist . Because of the reason is that this maybe can success to bring a large profit . Predict and forecast with arima model can apply in various situation . For example , arima predict can forecast daily sale volume , and generate next day , next week , next month value of future sale . Industry IOT connect the component output a day , to forecast the product rate of the industry company . Web scrap can get the social media hot issue , monitor the issue click , also mean popular . Observation the issue interval time can predict the issue news whether up or down , when is the issue will disappear , or get more popular . Web scrap also can request the bank's foreign coin website , to forecasting some coin value . Government open data can be used by arima predict model to forecast government and society trend . Economic researcher use arima technique to fit model , and analysis predicting the GDP . Temperature sensor generate air humid real time , it even have the possible that using arima to predict humid with sensor operate in small time slise to forecast last hour humid . In begin discuss to stock prrdict , arima also can be applied with stock predict using with the linear fit , the solution of linear regress to deal with stock predict .