Abstract
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.
Highlights
Chart analysis is a kind of technical analysis in financial trading, which is different from quantitative analysis
In order to generate the stored pattern that could be properly learned by the Hopfield neural network (HNN), we present three different representation: (a) Perceptually Important Points (PIP)-TG: To reduce the processing time, we firstly utilize PIP to process the data and extract the data points to generate pattern identification code (PIC), such that a simplified TG can be formed. (b) N-equal-part TG: Preset the dimension N of the template grid, the time sequence would be split evenly into N parts, each part represents the data point on the TG
We proposed a lightweight pattern matching method by utilizing the learning associative Hopfield network, which is a non-distance-based approach that combines the segmented representation method
Summary
Chart analysis is a kind of technical analysis in financial trading, which is different from quantitative analysis. How to find the subsequences that match the query patterns as much as possible [3] has become an important problem in technical analysis This question can be explained as given a fixed length of the financial time series data, find all the subsequences similar to the stored or expected pattern like H&S. The Euclidean distance (ED) method can be used to calculate the similarity of two patterns and it does not need to be segmented, but from the previous experiments we can see that the ED approach has bad performance regard to some distorted sequences data and does not consider the horizontal and vertical shifts, so the dynamic time warping [15] algorithm (DTW) would be more useful in time-series data processing. We leverage HNN’s advantage in warping pattern recognition and the segmentation method in our work, proposing a training-based pattern matching approach, which only needs to be trained on the predefined template pattern.
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