@misc{Gürsakal_Necmi_Finding_2020,
 author={Gürsakal, Necmi and Yilmaz, Firat Melih and Uğurlu, Erginbay},
 identifier={DOI: 10.15611/eada.2020.3.01},
 year={2020},
 rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy},
 publisher={Uniwersytet Ekonomiczny we Wrocławiu},
 description={Econometrics = Ekonometria, 2020, Vol. 24, No. 3, s. 1-19},
 language={eng},
 abstract={Data have shapes, and human intelligence and perception have to classify the forms of data to understand and interpret them. This article uses a sliding window technique and the main aim is to answer two questions. Is there an opportunity window in time series of stock exchange index? The second question is how to find a way to use the opportunity window if there is one. The authors defined the term opportunity window as a window that is generated in the sliding window technique and can be used for forecasting. In analysis, the study determined the different frequencies and explained how to evaluate opportunity windows embedded using time series data for the S&P 500, the DJIA, and the Russell 2000 indices. As a result, for the S&P 500 the last days of the patterns 0111, 1100, 0011; for the DJIA the last days of the patterns 0101, 1001, 0011; and finally for the Russell 2000, the last days of the patterns 0100, 1001, 1100 are opportunity windows for prediction},
 title={Finding opportunity windows in time series data using the sliding window technique: The case of stock exchanges},
 type={artykuł},
 keywords={time series, data science, patterns, sliding window, szeregi czasowe, nauka o danych, wzorce},
}