An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neura...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 9; p. 3876
Main Authors Mai, Weiming, Lee, Raymond S. T.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2021
Subjects
Algorithms
chart analysis
Data processing
Euclidean geometry
Experiments
learning associate hopfield network
Methods
Neural networks
Pattern analysis
Pattern matching
Pattern recognition
perceptually important points
Quantitative analysis
Segmentation
Similarity measures
Support vector machines
Time series
time series data mining
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Summary:Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11093876