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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2017
عنوان انگلیسی مقاله:
Pattern graph tracking-based stock price prediction using big data
ترجمه فارسی عنوان مقاله:
پیش بینی قیمت سهام مبتنی بر ردیابی الگو با استفاده از داده های بزرگ
منبع:
Sciencedirect - Elsevier - Future Generation Computer Systems, Accepted manuscript. doi:10.1016/j.future.2017.02.010
نویسنده:
Seungwoo Jeon, Bonghee Hong, Victor Chang
چکیده انگلیسی:
Stock price forecasting is the most difficult field owing to irregularities. How
ever, because stock prices sometimes show similar patterns and are determined
by a variety of factors, we propose determining similar patterns in historical stock
data to achieve daily stock prices with high prediction accuracy and potential rules
for selecting the main factors that significantly affect the price, while simultane
ously considering all factors. This study is intended at suggesting a new complex
methodology that finds the optimal historical dataset with similar patterns accord
ing to various algorithms for each stock item and provides a more accurate pre
diction of daily stock price. First, we use a Dynamic Time Warping algorithm to
find patterns with the most similar situation adjacent to a current pattern. Second,
we select the determinants most affected by the stock price using feature selec
tion based on Stepwise Regression Analysis. Moreover, we generate an artificial
neural network model with selected features as training data for predicting the
best stock price. Finally, we use Jaro-Winkler distance with Symbolic Aggregate
approXimation (SAX) as a prediction accuracy measure to verify the accuracy of
our model.
Keywords: Stock price prediction| Dynamic time warping| Feature selection|Artificial neural network| Jaro-Winkler distance| Symbolic Aggregate approXimation
قیمت: رایگان
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