E-Locus - Ιδρυματικό Καταθετήριο Πανεπιστημίου Κρήτης - Mining chart patterns in financial asset data

Αρχική Mining chart patterns in financial asset data

Αποτελέσματα - Λεπτομέρειες

[Προσθήκη στο καλάθι]

Τίτλος

Mining chart patterns in financial asset data

Άλλος τίτλος

Εξόρυξη μοτίβων γραφημάτων σε δεδομένα χρηματοοικονομικών περιουσιακών στοιχείων

Συγγραφέας

Νικολάου, Κωνσταντίνος

Σύμβουλος διατριβής

Ζέζας, Ανδρέας

Περίληψη

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.

Γλώσσα

Αγγλικά

Ημερομηνία έκδοσης

2022-11-25

Συλλογή

Σχολή/Τμήμα--Σχολή Θετικών και Τεχνολογικών Επιστημών--Τμήμα Φυσικής--Πτυχιακές εργασίες

Τύπος Εργασίας--Πτυχιακές εργασίες

Εμφανίσεις

569

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