时间序列分析外文翻译资料

Time Series Analysis

Data mining is the analysis of observed data sets in order to find the models and to summarize the data in the new ways that are meant for both understandable and useful. Data arriving in time order arises in fields ranging from many other areas of physics, finance, medicine, music, and so on. The time series is an important class of temporal data objects and they can be easily obtained from financial and scientific applications. Time series analysis comprises methods and techniques for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Given the spread of the appearance of time series data, and the exponentially growing sizes of databases, there has been recently been an explosion of interest in time series data mining. As extremely large time series data sets grow more prevalent in a wide variety of settings, this thesis faces the significant challenge of developing efficient analysis methods. The researches in this thesis address the problem in designing fast, scalable algorithms for the analysis of time series.

The research on time series analysis with the tasks such as preprocessing and transformation data for the prediction purpose has a meaningful and popular in the case of big size data. If the data or time series data in particular can be preprocessed so as to improve the efficiency and lack of difficulty of the mining and discovering processes. There are a lot of data preprocessing data techniques; to remove the noise and correct incompatibilities in data, the cleaning techniques can be applied; to merge data from multi sources into coherent data storage, the integration techniques can be used; to normalize data, the transformation techniques can be referred. Data reduction is one of the meaningful techniques in the preprocessing stage of time series analysis can reduce the data size by collecting, eliminating redundant features. In general, time series predictability is a measure of how well future values of a time series can be predicted, where a time series is a sequence of observations. Time series predictability indicates to what extent the past can be used to determine the future in a time series. A time series generated by a deterministic linear process has high predictability, and its future values can be predicted very well from the past values. A time series generated by an uncorrelated process has low predictability, and its past values provide only a statistical characterization of the future values.

This thesis makes four major contributions:

Firstly, we propose the data preprocessing method to reduce the dimensions of time series in terms of the keeping the shape when compared to the original data in this thesis. The method based on the idea of turning points in a time series; these points are defined as the change in the trend of the time series data. The turning points in time series are defined as the points that separate two adjacent trends and have the shortest distance from the release time of announcements. Only some of the critical points are preserved; those critical points, which are considered as interference factors are removed. This method only considers the critical points of each time series in a certain period in order to reduce the data size by eliminating redundant features. This data preprocessing method, when applied before mining process, can significantly make better the overall quality of the patterns mined and the time required for the actual mining. All of dimensionality reduction techniques are very meaningful to preprocess the large dataset and then use it to analyze and discover knowledge.

Secondly, the next contribution mentioned in this thesis is the proposed method of analysis trend of the time series. This function is a short term prediction; this term is related to one-step-ahead prediction. The results of the combination method are the predicted values which would be used for making the decisions by the trading rules. In this task, the clustering is first the procedure of collecting the data into clusters; hence all the objects within a cluster will have higher similarity than in comparison to one another but are very dissimilar to objects in other clusters. After that, we consider the data classification procedure, where a classifier is constructed to predict trend labels, such as “upward”, “no-trend” or “downward” for the financial data. The classification process for prediction trend implements in two sub-processes: learning and classification. The learning sub-process analyzes data by support vector machine and the learned classifier is represented in the form of classification rules. Then the next sub- process estimated the accuracy of test data depend on classification rules. In the case of the accuracy is measured suitable, the rules can be applied to the classification of new future values.

Thirdly, the next contribution is the proposed method for predicting the future values depend on historical values in the multiple time series environment. We think that it is an important component of procedures research because these data results often supply the foundation for decision making models. Modeling the time series data is a type of statistical issue; and time series prediction techniques have been used in many real world applications. Prediction techniques are used in computational procedures to estimate the parameters of a model being used to allocate limited resources or to describe random processes such as those mentioned above. And the problem of time series predictive analysis of the environment with multiple time series also mentioned in this thesis. In learning machine approach, the support vector machine can be used for regression is called support vector regression, support vector regression has been applied successfully to stream time series analysis, but its optimization algorithm is usually buil

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Time Series Analysis

Data mining is the analysis of observed data sets in order to find the models and to summarize the data in the new ways that are meant for both understandable and useful. Data arriving in time order arises in fields ranging from many other areas of physics, finance, medicine, music, and so on. The time series is an important class of temporal data objects and they can be easily obtained from financial and scientific applications. Time series analysis comprises methods and techniques for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Given the spread of the appearance of time series data, and the exponentially growing sizes of databases, there has been recently been an explosion of interest in time series data mining. As extremely large time series data sets grow more prevalent in a wide variety of settings, this thesis faces the significant challenge of developing efficient analysis methods. The researches in this thesis address the problem in designing fast, scalable algorithms for the analysis of time series.

The research on time series analysis with the tasks such as preprocessing and transformation data for the prediction purpose has a meaningful and popular in the case of big size data. If the data or time series data in particular can be preprocessed so as to improve the efficiency and lack of difficulty of the mining and discovering processes. There are a lot of data preprocessing data techniques; to remove the noise and correct incompatibilities in data, the cleaning techniques can be applied; to merge data from multi sources into coherent data storage, the integration techniques can be used; to normalize data, the transformation techniques can be referred. Data reduction is one of the meaningful techniques in the preprocessing stage of time series analysis can reduce the data size by collecting, eliminating redundant features. In general, time series predictability is a measure of how well future values of a time series can be predicted, where a time series is a sequence of observations. Time series predictability indicates to what extent the past can be used to determine the future in a time series. A time series generated by a deterministic linear process has high predictability, and its future values can be predicted very well from the past values. A time series generated by an uncorrelated process has low predictability, and its past values provide only a statistical characterization of the future values.

This thesis makes four major contributions:

Firstly, we propose the data preprocessing method to reduce the dimensions of time series in terms of the keeping the shape when compared to the original data in this thesis. The method based on the idea of turning points in a time series; these points are defined as the change in the trend of the time series data. The turning points in time series are defined as the points that separate two adjacent trends and have the shortest distance from the release time of announcements. Only some of the critical points are preserved; those critical points, which are considered as interference factors are removed. This method only considers the critical points of each time series in a certain period in order to reduce the data size by eliminating redundant features. This data preprocessing method, when applied before mining process, can significantly make better the overall quality of the patterns mined and the time required for the actual mining. All of dimensionality reduction techniques are very meaningful to preprocess the large dataset and then use it to analyze and discover knowledge.

Secondly, the next contribution mentioned in this thesis is the proposed method of analysis trend of the time series. This function is a short term prediction; this term is related to one-step-ahead prediction. The results of the combination method are the predicted values which would be used for making the decisions by the trading rules. In this task, the clustering is first the procedure of collecting the data into clusters; hence all the objects within a cluster will have higher similarity than in comparison to one another but are very dissimilar to objects in other clusters. After that, we consider the data classification procedure, where a classifier is constructed to predict trend labels, such as “upward”, “no-trend” or “downward” for the financial data. The classification process for prediction trend implements in two sub-processes: learning and classification. The learning sub-process analyzes data by support vector machine and the learned classifier is represented in the form of classification rules. Then the next sub- process estimated the accuracy of test data depend on classification rules. In the case of the accuracy is measured suitable, the rules can be applied to the classification of new future values.

Thirdly, the next contribution is the proposed method for predicting the future values depend on historical values in the multiple time series environment. We think that it is an important component of procedures research because these data results often supply the foundation for decision making models. Modeling the time series data is a type of statistical issue; and time series prediction techniques have been used in many real world applications. Prediction techniques are used in computational procedures to estimate the parameters of a model being used to allocate limited resources or to describe random processes such as those mentioned above. And the problem of time series predictive analysis of the environment with multiple time series also mentioned in this thesis. In learning machine approach, the support vector machine can be used for regression is called support vector regression, support vector regression has been applied successfully to stream time series analysis, but its optimization algorithm

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