چکیده مقاله
In the area of big data, Google trends and its forecasting power have received considerable attention among scientists In this paper, we investigate the ability of Google trend data to forecast new cases of Covide 19 in Iran We employed a supervised machine learning method known as GMDH type neural network The searching data of several Covid 19 related terms in the Persian language are used as predictors of Covid 19 new cases over the period2020/10/24 to 2021/06/22 Five models with different input variables are investigated The results show that the RMSE of the models varies between 10 81 and 8 152, and the lowest RMSE occurs in the model in which the seven day lag of COVID 19 new cases is entered as an input variable Our tool can improve the policymakers' and researchers’ understating from spreading pandemic during a challenging time when the infection rate is significantly high, andofficial statistics cannot be reliable
کلیدواژهها
نویسندگان
Maryam Seifaddini
Department of Computer Science Assistant professor , Faculty of Mathematics University of Guilan, Rasht, Iran
Amir Habibdoust
Department of Economics and Accounting Lecturer , University of Guilan, Rasht, Iran
شیوه ارجاع
Seifaddini, Maryam and Habibdoust, Amir,1400,Can Google Trends Data Predict Next Covid-19 Peak? A Machine Leaning Approach,Fourth International Conference on Soft Computing
ارائهشده در
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