Forecasting Gold Price Using Data Mining Technique by Considering New Features and Comparison Between New Features and Original Data

چکیده مقاله

The gold price forecast is similar to the forecasting of other financial time series In this paper, we make forecasts using data mining methods The data set used in this article is the gold price data set and it s from 1985 to 2019 and it has 8569 rows In this paper, we try to achieve better forecasting by defining more useful features on the dataset and eliminating some inefficient features The five features of the high minus low percent HML , price changes, random oscillator, %D and position, are defined on the dataset and their effects on model are examined These investigations are performed by three data mining algorithms i e SVR, Random Forest Regression and KNN Finally, among all the algorithms we tried, we selected an algorithm that has lower RMSE and calls it the best algorithm among the algorithms we used for model prediction

کلیدواژه‌ها

نویسندگان

Fateme Zahra Darzi

MSc student; Alzahra university;

Roshanak Alimohammadi

Associate professor; Alzahra university;

Seyed Bagher Mirashrafi

Associate professor; Mazandaran university;

Darzi, Fateme Zahra and Alimohammadi, Roshanak and Mirashrafi, Seyed Bagher,1398,Forecasting Gold Price Using Data Mining Technique by Considering New Features and Comparison Between New Features and Original Data,3rd International Conference on Soft Computing,Rudsar

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مجموعه مقالات سومین کنفرانس بین المللی محاسبات نرم29 آبان 1398 · رودسر