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Title

Mining chart patterns in financial asset data

Alternative Title

Εξόρυξη μοτίβων γραφημάτων σε δεδομένα χρηματοοικονομικών περιουσιακών στοιχείων

Author

Νικολάου, Κωνσταντίνος

Thesis advisor

Ζέζας, Ανδρέας

Abstract

Time Series Classification (TSC) problems are a rising subject of re- search in the world of machine and deep learning. Many methods have been developed in the last two decades over problems such as voice and image recognition, with many everyday applications. While financial asset price charts have been a focal example of time series, recognizing charac- teristic patterns that may give away the future trend of an asset’s price is a more novel method.In this paper we attempted to transform real- valued data with SAX(Symbolic Aggregate approXimation), and created a novel rule-based approach to extract patterns as strings of characters, as well as an algorithm called CPC-SAX to predict unlabeled data, through weighted distance of the characters of each string. The results show high accuracy, for 12 characteristic chart patterns and four different time win- dows of 15,30,45, and 60 days.The correlation between the appearance of patterns and time windows is also highlighted. We aspire to add more chart patterns in the labelling process and refine both the rule-based ap- proach and distanced-based prediction of the algorithm, in future work.

Language

English

Issue date

2022-11-25

Collection

School/Department--School of Sciences and Engineering--Department of Physics--Graduate theses

Type of Work--Graduate theses

Views

498

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