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Time series visualization is a fundamental task in most financial applications. A framework that can reduce the dimensionality of the time series data, sufficiently accurate so that it can capture the actual shape of the time series but, at the same time, salient points will not be smoothed out can take advantage of charting the raw time series data. On the other hand, it is preferable that the representation framework can handle the multi-resolution problem rather than reduce the dimension to a fixed level only. In this paper, a framework that represents and visualizes time series data based on data point importance is proposed. Furthermore, discovering frequently appearing and surprising patterns are non-trivial tasks in financial applications. A method for discovering patterns across different resolutions is proposed. The proposed method is based on a modified version of VizTree. By converting the time series to symbol string based on data point importance, the potential patterns with different lengths can be encoded in the VizTree for visual pattern discovery while the important points and the overall shape of the time series patterns can be preserved even under a high compression ratio. Various experiments were conducted to evaluate the performance of the proposed framework. One may find it particularly attractive in financial applications like stock data analysis.

Recently, the increasing use of time series data has initiated various research and development attempts in the field of data and knowledge management. Time series data is characterized as large in data size, high dimensionality and update continuously. Moreover, the time series data is always considered as a whole instead of individual numerical fields. Indeed, a large set of time series data is from stock market. Stock time series has its own characteristics over other time series. Moreover, dimensionality reduction is an essential step before many time series analysis and mining tasks. For these reasons, research is prompted to augment existing technologies and build new representation to manage financial time series data. In this paper, financial time series is represented according to the importance of the data points. With the concept of data point importance, a tree data structure, which supports incremental updating, is proposed to represent the time series and an access method for retrieving the time series data point from the tree, which is according to their order of importance, is introduced. This technique is capable to present the time series in different levels of detail and facilitate multi-resolution dimensionality reduction of the time series data. In this paper, different data point importance evaluation methods, a new updating method and two dimensionality reduction approaches are proposed and evaluated by a series of experiments. Finally, the application of the proposed representation on mobile environment is demonstrated.

One of the major duties of financial analysts is technical analysis. It is necessary to locate the technical patterns in the stock price movement charts to analyze the market behavior. Indeed, there are two main problems: how to define those preferred patterns (technical patterns) for query and how to match the defined pattern templates in different resolutions. As we can see, defining the similarity between time series (or time series subsequences) is of fundamental importance. By identifying the perceptually important points (PIPs) directly from the time domain, time series and templates of different lengths can be compared. Three ways of distance measure, including Euclidean distance (PIP-ED), perpendicular distance (PIP-PD) and vertical distance (PIP-VD), for PIP identification are compared in this paper. After the PIP identification process, both template- and rule-based pattern-matching approaches are introduced. The proposed methods are distinctive in their intuitiveness, making them particularly user friendly to ordinary data analysts like stock market investors. As demonstrated by the experiments, the template- and the rule-based time series matching and subsequence searching approaches provide different directions to achieve the goal of pattern identification.

In this paper, we present our design of a GIS (Geographic Information System) to provide tourist information to visitors via the WWW. The system can help a tourist, even when he is not in Hong Kong, to find information about routes, buildings, underground railways and train stations. Further, the GIS also allows a tourist to interact with the displayed map by zooming, scrolling, panning, finding out a walking path between two locations with a time estimate, and linking to other WWW locations of tourist hotspots in Hong Kong.

It is always expected that a correlation exists between the movement of stock prices (technical analysis) and news sentiment (fundamental analysis). If we can determine such a correlation, further interesting research directions will certainly be generated. In this paper, a system prototype is proposed for investigating the correlation between stock prices and news sentiment. Our primary target market is Hong Kong and the system is customized for Chinese language. Different methods and the impacts of various design parameters are tested in the experiments.

SB-Tree is a data structure proposed to represent time series according to the importance of the data points. Its advantages over traditional time series representation approaches include: representing time series directly in time domain (shape preservation), retrieving time series data according to the importance of the data points and facilitating multi-resolution time series retrieval. Based on these benefits, one may find this representation particularly attractive in financial time series domain and the corresponding data mining tasks, i.e. categorization and clustering. In this paper, an investigation on the size of the SB-Tree is reported. Two SB-Tree optimization approaches are proposed to reduce the size of the SB-Tree while the overall shape of the time series can be preserved. As demonstrated by various experiments, the proposed approach is suitable for different categorization and clustering applications.

SB-Tree is a binary tree data structure proposed to represent time series according to the importance of data points. Its use in stock data management is distinguished by preserving the critical data points' attribute values, retrieving time series data according to the importance of data points and facilitating multi-resolution time series retrieval. As new stock data are available continuously, an effective updating mechanism for SB-Tree is needed. In this paper, a study of different updating approaches is reported. Three families of updating methods are proposed. They are periodic rebuild, batch update and point-by-point update. Their efficiency, effectiveness and characteristics are compared and reported.

Nowadays, financial applications spread over various devices in both e-commerce and m-commerce. One of the major tasks in this kind of application is viewing the historical price movement of a stock for the market players before making any decision. However, due to the divergence of the configurations among different devices and platforms, adaptation is necessary for time series data delivery and visualization. In this paper, an adaptive framework of financial time series delivery and visualization is proposed to achieve this goal. The core system adapts the concept of data point importance and data points reordering for time series representation. The application of the proposed framework on stock price time series delivery and visualization is demonstrated in different devices using Web service.

In our previous work, a time series representation framework, specialized binary tree (SB-tree) has been proposed for representing the stock time series data effectively and efficiently. By putting a set of SB-trees together, a time series database is formed while we termed it as a specialized binary tree-forest (i.e. SBT-forest). By manipulating the SBT-forest, different time series query and mining processes can be facilitated. However, the major challenge is how to locate a SB-tree in the forest efficiently. Therefore, the development of an indexing approach for the SB-trees is of fundamental importance for maintaining an acceptable speed for query. In this paper, a time series indexing approach, based on transforming the SB-trees to symbol strings first and then indexing the symbol strings by a trie data structure, is proposed. The proposed approach is efficient and effective as well. As demonstrated in the experiments, the proposed approach speeds up the time series query process. The proposed approach can handle the problem of updating new entries to the database without any difficulty.

Discovery of interesting or frequently appearing time series patterns is one of the important tasks in various time series data mining applications. However, recent research criticized that discovering subsequence patterns in time series using clustering approaches is meaningless. It is due to the presence of trivial matched subsequences in the formation of the time series subsequences using sliding window method. The objective of this paper is to propose a threshold-free approach to improve the method for segmenting long stock time series into subsequences using sliding window. The proposed approach filters the trivial matched subsequences by changing Perceptually Important Point (PIP) detection and reduced the dimension by PIP identification.