The Motif Detection of Turning Points in Stock Time Series

2017 ◽  
pp. 520-524
Author(s):  
Zhiyuan Zhou ◽  
Wengang Che ◽  
Pengyu Quan ◽  
Jing Long
Keyword(s):  
Time Series ◽  
Turning Points ◽  
Motif Detection

Can univariate models forecast turning points in seasonal economic time series?

1998 ◽  
Vol 14 (4) ◽  
pp. 433-446 ◽  
Author(s):  
Antonio Garcı́a-Ferrer ◽  
Ricardo A. Queralt
Keyword(s):  
Time Series ◽  
Turning Points ◽  
Economic Time Series ◽  
Economic Time

scholarly journals A Mathematical Modeling Framework To Detect The Optimal Financial Turning Points

2021 ◽  
Author(s):  
Fateme Yazdani ◽  
Mehdi Khashei ◽  
Seyed Reza Hejazi
Keyword(s):  
Mathematical Modeling ◽  
Time Series ◽  
Financial Time Series ◽  
Turning Points ◽  
Real Data ◽  
Detection Methods ◽  
Modeling Framework ◽  
Detection Model ◽  
Financial Time ◽  
History Of

Abstract The financial markets have always witnessed the competition of their participants for gaining high and stable profits. The realization extent of this goal depends on the profitability of the trading points or turning points (TPs) ahead. TPs prediction problem is one of the most challenging yet important problems in the financial discipline. The first step towards predicting financial TPs is to detect TPs from the history of the corresponding financial time series. Literature indicates that the profitability of the predicted financial TPs depends on the profitability of the detected TPs. Given this, numerous efforts have been devoted to enhancing the profitability of the detected financial TPs. Nevertheless, to the best of our knowledge, none of the existing detection methods can detect the most profitable or the optimal TPs from the history of financial time series. The present study concerns this research gap and ensures detecting the optimal financial TPs by proposing a mathematical modeling framework. The proposed optimal TPs detection model in this paper will be structured concerning the three following assumptions. First, short-selling the financial asset is possible. Second, the time value for the investment money is not considered. Third, detecting consecutive buying TPs and consecutive selling TPs is not allowed. Empirical results with twenty real data sets indicate that the proposed model, in contrast to the existing TPs detection methods, detects the optimal TPs from the history of the financial time series.

On-line motif detection in time series with SwiftMotif

2009 ◽  
Vol 42 (11) ◽  
pp. 3015-3031 ◽  
Author(s):  
Erich Fuchs ◽  
Thiemo Gruber ◽  
Jiri Nitschke ◽  
Bernhard Sick
Keyword(s):  
Time Series ◽  
Motif Detection ◽  
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