Search for Patterns in Compressed Time Series

Pratt, Kevin; Fink, Eugene

doi:10.1142/s0219467802000482

Cited by 120 publications

(47 citation statements)

References 24 publications

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“…Now we adopt a measure to analyze the difference between time series of network entropies. Following the study of Pratt and Fink [41], we use the root mean square difference to measure the difference between time series. The root mean square difference (RD) between {a1, ..., an} and {b1, ..., bn} is given as follows: From Figures 4 and 5, it can be seen that there are high values for the Renyi and Tsallis entropies during the crisis for α > 0, and low values for the same entropies during the crisis when α < 0.…”

Section: Resultsmentioning

confidence: 99%

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Network Entropies of the Chinese Financial Market

2016

Entropy

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Based on the data from the Chinese financial market, this paper focuses on analyzing three types of network entropies of the financial market, namely, Shannon, Renyi and Tsallis entropies. The findings suggest that Shannon entropy can reflect the volatility of the financial market, that Renyi and Tsallis entropies also have this function when their parameter has a positive value, and that Renyi and Tsallis entropies can reflect the extreme case of the financial market when their parameter has a negative value.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Network Entropies of the Chinese Financial Market

2016

Entropy

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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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“…For querying the Landmark sequence, they recommended that the Landmark sequence must be more similar to a string sequence, rather than a multidimensional sequence; thus, string indexing is more suitable than the use of R-tree. Similar concepts regarding the identification of important points can be found in studies conducted in (Pratt and Fink, 2002;Fink and Pratt, 2003). They compressed a time series by selecting some of the minima and maxima or important points of peaks and troughs and dropping the other points.…”

Section: Introductionmentioning

confidence: 87%

Index Financial Time Series Based on Zigzag-Perceptually Important Points

Phetchanchai

Selamat²,

Rehman

et al. 2010

Journal of Computer Science

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Problem statement: Financial time series were usually large in size, unstructured and of high dimensionality. Since, the illustration of financial time series shape was typically characterized by a few number of important points. These important points moved in zigzag directions which could form technical patterns. However, these important points exhibited in different resolutions and difficult to determine. Approach: In this study, we proposed novel methods of financial time series indexing by considering their zigzag movement. The methods consist of two major algorithms: first, the identification of important points, namely the Zigzag-Perceptually Important Points (ZIPs) identification method and next, the indexing method namely Zigzag based M-ary Tree (ZM-Tree) to structure and organize the important points. Results: The errors of the tree building and retrieving compared to the original time series increased when the important points increased. The dimensionality reduction using ZM-Tree based on tree pruning and number of retrieved points techniques performed better when the number of important points increased. Conclusion: Our proposed techniques illustrated mostly acceptable performance in tree operations and dimensionality reduction comparing to existing similar technique like Specialize Binary Tree (SB-Tree).

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Search for Patterns in Compressed Time Series

Cited by 120 publications

References 24 publications

Network Entropies of the Chinese Financial Market

Network Entropies of the Chinese Financial Market

Financial Data Representation and Similarity Model

Index Financial Time Series Based on Zigzag-Perceptually Important Points

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