Stock time series pattern matching: Template-based vs. rule-based approaches

Fu, Tak-chung; Chung, Fu-Lai; Luk, Robert W. P.; Ng, Chak-man

doi:10.1016/j.engappai.2006.07.003

Cited by 99 publications

(48 citation statements)

References 7 publications

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“…The result of similarity measured by radian distance is exactly the same as Euclidean distance, even sorted by the distance value for Foreign Exchange Rate data. In addition, to illustrate that the RadDist is segmentation algorithm independent, we conducted two more experiments by using PLR_PF [11] and PLR_PIP [13] to compress the time series respectively. Both PLR_PF and PLR_PIP are classical piecewise linear segmentation algorithms.…”

Section: Resultsmentioning

confidence: 99%

Financial Data Representation and Similarity Model

Ding¹,

Yang²,

Kavs³

et al. 2010

IJTEF

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Abstract-Similarity metric is of fundamental importance for similarity matching and subsequence query in time series applications. Most existing approaches measure the similarity by calculating and aggregating the point-to-point distance, few of them take the segment trend duration into account. In this paper, upon analyzing the properties of financial time series, we define a time series notation which is more intuitive and expressive. Base on that, a new similarity model is proposed. Experiments on both real foreign currency exchange rate data and stock market data are performed. The result shows the effectiveness and good accuracy of our method. The similarity model is also proved to be segmentation algorithm independent thus can be combined with other segmentations for similarity query, pattern matching, classification, and clustering.

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Section: Resultsmentioning

confidence: 99%

Financial Data Representation and Similarity Model

Ding¹,

Yang²,

Kavs³

et al. 2010

IJTEF

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“…Up to now, many literatures and researchers research and develop this theory. Such as literature [1], [2], [3], [11], [12], [13], [14], [15], [16], [17], [18]. This paper modifies the above research and proposed a new forecasting method of fuzzy time series.…”

Section: A Comparison Of Different Forecasting Modelsmentioning

confidence: 99%

“…For example some scholars research Stock Market [3] ~ [5], some research fuzzy data mining for time-series data [6] ~ [8], Li H, Kozma R. who research A dynamic neural network method for time series prediction using the KIII model [9] and so on. Zadeh (1965) publish creativity essay of Fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

A Modified Method of Forecasting Enrollments Based on Fuzzy Time Series

Feng¹,

Wang²,

Guo³

et al. 2014

Proceedings of the 2nd International Conference on Soft Computing in Information Communication Technology

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Abstract---In this essay, we propose a method of forecasting enrollments based on fuzzy time series which is improved by Saxena, Sharma& Easo. Some sections are improved, Such as the setting of universe of discourse, construction of fuzzy set and Inverse fuzzy number, and forecasting formula. We still used the 22 years freshmen's enrollments data of the University of Alabama to illustrate the forecasting process. The result shows that proposed method provide the smallest AFER and MSE.

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“…Thanks to its applicability in detecting various kinds of patterns, successful applications in a variety of different research fields have been reported. Such fields include eye-region detection [5,6], human authentication [7], stock change categorization [8], handwriting recognition [9], signature verification [10], and electroencephalogram (EEG) artifact detection [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Template Matching Using Dynamic Positional Warping for Identification of Specific Patterns in Electroencephalogram

Chang

2014

Journal of Applied Mathematics

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Template matching is an approach for signal pattern recognition, often used for biomedical signals including electroencephalogram (EEG). Since EEG is often severely contaminated by various physiological or pathological artifacts, identification and rejection of these artifacts with improved template matching algorithms would enhance the overall quality of EEG signals. In this paper, we propose a novel approach to improve the accuracy of conventional template matching methods by adopting the dynamic positional warping (DPW) technique, developed recently for handwriting pattern analysis. To validate the feasibility and superiority of the proposed method, eye-blink artifacts in the EEG signals were detected, and the results were then compared to those from conventional methods. DPW was found to outperform the conventional methods in terms of artifact detection accuracy, demonstrating the power of DPW in identifying specific one-dimensional data patterns.

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Stock time series pattern matching: Template-based vs. rule-based approaches

Cited by 99 publications

References 7 publications

Financial Data Representation and Similarity Model

Financial Data Representation and Similarity Model

A Modified Method of Forecasting Enrollments Based on Fuzzy Time Series

Enhanced Template Matching Using Dynamic Positional Warping for Identification of Specific Patterns in Electroencephalogram

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