Effect of segmentation on financial time series pattern matching

Wan, Yuqing; Gong, Xueyuan; Si, Yain-Whar

doi:10.1016/j.asoc.2015.10.012

Cited by 37 publications

(18 citation statements)

References 17 publications

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“…Recent studies have found that different segmentation methods can affect the pattern matching results. In [12], the efficiency and the effectiveness of TB, RB and PAA when they are paired with PIP-VD were investigated. According to [12], the performance of TB and RB were superior to the PAA-based method.…”

Section: Distance Based Approaches For Time Series Classificationmentioning

confidence: 99%

Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns

Lei

Teh

2021

Applied Soft Computing

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In financial markets, appearances of chart patterns in time series are commonly considered as potential signals for imminent change in the direction of price movement. To identify chart patterns, time series data is usually segmented before it can be processed by different classification methods. However, existing segmentation methods are less effective in classifying 16 curve-shaped chart patterns from financial time series. In this paper, we propose three novel segmentation methods for classification of curveshaped chart patterns based on direct least squares fitting of ellipses. These methods are implemented based on the principles of sliding windows, turning points, and bottom-up piece wise linear approximation. To further enhance the efficiency of classifying chart patterns from real-time streaming data, we propose a novel algorithm called Accelerating Classification with Prioritized Rules (ACPR). Experiments based on real datasets from financial markets reveal that the proposed approaches are effective in classifying curveshaped patterns from time series. Experiment results reveal that the proposed segmentation methods with ACPR can significantly reduce the total execution time.

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Section: Distance Based Approaches For Time Series Classificationmentioning

confidence: 99%

Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns

Lei

Teh

2021

Applied Soft Computing

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“…usually, w 1 is set to be 0.5 in the experiment of [2,3,5], we follow this setting in our experiment. Furthermore, we set a threshold for the similarity measure: Once D(SP, Q) is lower than the preset threshold, the stored pattern with the minimum D would be accepted as the matching pattern.…”

Section: Related Workmentioning

confidence: 99%

“…Our experiment would be conducted on the synthetic dataset following the algorithm in Wan's paper [5] to generate the synthetic data. It consists of three steps to generate the data, time scaling, time warping, and noise adding.…”

Section: Synthetic Data and Predefined Templatesmentioning

confidence: 99%

“…The existing time series data mining research has been studied thoroughly in [4][5][6]. Fu et al proposed a preprocessing method called perceptually important point (PIP) [3,7] to reduce the fluctuant data points and extract a given number of points to represent the subsequence of the time series data.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, Si et al [10] proposed a segmentation method based on TPs (turning points). Wan et al conducted several experiments [5] of different segmentation methods on the synthetic data and concluded that the PIP segmentation is robust in different similarity measurement and can preserve the overall shape of the subsequence.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Mai

Lee

2021

Applied Sciences

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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.

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Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms

Adegboye

Kampouridis

Otero

2021

Int J of Intelligent Sys

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Effect of segmentation on financial time series pattern matching

Cited by 37 publications

References 17 publications

Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns

Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns

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

Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms

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