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Pattern Recognition in Temporal Data
Hovanec, Stanislav ; Hynčica, Ondřej (referee) ; Honzík, Petr (advisor)
This diploma work initially conduct research in the area of descriptions and analysis of time series. The thesis then proceed to introduce the problems of technical analysis of price charts as well as indicators, price patterns and method of Pure Price Action. The method Pure Price Action is demonstrated in this work in two practical examples of its application to real businesses with a view to discovering and analyzing price patterns, as well as analysis and prediction of future price and financial evolution. This analysis is an introduction to the processes of successful business, following on from this we discuss the theme of Pattern Recognition and the Instance Based Learning method. The practical aspect of this work is carried out with the aid of a MATLAB applied algorithm for the analysis of the price pattern Correction for sale and purchase in dynamic time segments, specifically in trading price graphs, like those used for commodities or stock trading. For the analysis of time series we use the Pure Price Action method. The Instance Based Learning method is used by the algorithm to recognize price patterns. The created algorithm is verified on real data of a 5 minute time series of the US Dow Jones price charts for the years 2006, 2007, 2008. The achieved accuracy is evaluated with the aid of Equity Curves.
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Pattern Recognition in Temporal Data
Hovanec, Stanislav ; Hynčica, Ondřej (referee) ; Honzík, Petr (advisor)
This diploma work initially conduct research in the area of descriptions and analysis of time series. The thesis then proceed to introduce the problems of technical analysis of price charts as well as indicators, price patterns and method of Pure Price Action. The method Pure Price Action is demonstrated in this work in two practical examples of its application to real businesses with a view to discovering and analyzing price patterns, as well as analysis and prediction of future price and financial evolution. This analysis is an introduction to the processes of successful business, following on from this we discuss the theme of Pattern Recognition and the Instance Based Learning method. The practical aspect of this work is carried out with the aid of a MATLAB applied algorithm for the analysis of the price pattern Correction for sale and purchase in dynamic time segments, specifically in trading price graphs, like those used for commodities or stock trading. For the analysis of time series we use the Pure Price Action method. The Instance Based Learning method is used by the algorithm to recognize price patterns. The created algorithm is verified on real data of a 5 minute time series of the US Dow Jones price charts for the years 2006, 2007, 2008. The achieved accuracy is evaluated with the aid of Equity Curves.
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