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We addressed two areas of concern regarding the analysis of a financial time series with a correlation structure, coarse graining (or renormalization) and the extraction of leading and lagging structures. We introduce the complex Hilbert principal component analysis to solve these two problems, and apply them to the time series of 33 Tokyo Stock Exchange industry indices and Tokyo Stock Price Index...
Network management actions require the retention of data representing the temporal evolution of network state, mainly in the form of time series. Nonetheless, storing and exploiting those measurements is becoming a challenge as the production rate of such data is continuously increasing and data lasting for long time periods are used. To scale up the storage and improve both the analysis and visualization...
With progress in the area of computer science, it is achievable to read, process, store and generate information out of the available data. Humongous amount of data is generated, which is of mixed type, including time-series, Boolean, spatial-temporal and alpha-numeric data. This data is generated at a very giant speed and volume, which makes difficult for the traditional clustering algorithms to...
Feature extraction plays an important role in machinery fault diagnosis and prognosis. The features extracted from time, frequency and time-frequency domains are widely investigated to describe the properties of overall signal from different perspectives, seldom considering the sequential characteristic of time-series signal in which the fault information may be embedded. This paper investigates a...
Every company listed on the London Stock Exchange is classified into an industry sector based on its primary activity, however, it may be both more interesting and valuable to group similarly performing companies based on their historical stock price record over a long period of time. Using fuzzy clustering analysis with a correlation-based metric, we obtain a more insightful categorization of the...
It was very difficult to acquire satisfy classification result only using spectral information and textual information on the broad land of China. This paper divided the land cover regions based on multivariate data to improve classification accuracy which were Pa, BT, DEM, AVHRR NDVI time-series data and IM. Through principal components analysis, the information percent of first three principal components...
In recent years, fault prediction method, which means forecast process fault in an early time based on the current condition of the system, has attracted more and more attention by companies and scientists. However, it still has many problems in this area, especially for its application in industrial process. In the present work, a multi-step ahead fault prediction method combining principle component...
Using the method of the principal component analysis of the three-dimensional data of time sequence, we analyze the process of informationization of 31 provinces and cities in China in the year 2005 and 2008, further we calculate the score on the base of the first main composition to the areas, and give the tables of their sequence, analyze the dynamic change in these different areas. Some constructive...
Grain prices are influenced by many factors including the climate, the level of household consumption, consumption structure, the structure of supply and demand of domestic and international stock and futures markets and the circulation system of the grain, have a nonlinear dynamic system characteristics and the evolution law, suitable for the application of chaotic time sequence to study the law...
Soybean prices are influenced by many factors including the climate, the level of household consumption, consumption structure, the structure of supply and demand of domestic and international stock and futures markets and the circulation system of the soybean, have a nonlinear dynamic system characteristics and the evolution law, suitable for the application of chaotic time sequence to study the...
The selection of features for classification, clustering and approximation is an important task in pattern recognition, data mining and soft computing. For real-valued features, this contribution shows how feature selection for a high number of features can be implemented using mutual information. Especially, the common problem for mutual information computation of computing joint probabilities for...
Prices of agricultural products are influenced by many factors including the climate, the level of household consumption, consumption structure, the structure of supply and demand of domestic and international stock and futures markets and the circulation system of agricultural products, have a nonlinear dynamic system characteristics and the evolution law, suitable for the application of chaotic...
Indices based on correlation or more subtle strategies are among the standard ways to infer dependencies (i.e., exchange of information or coupling) in aggregations of different systems observed in the time domain. We propose a new index based on Renyi entropy and confront it with other indices, studying if some of these techniques can recognize when we are observing the same system twice, even when...
Multivariate time series (MTS) data sets are common in many multimedia, medical, process industry and financial applications such as gesture recognition, video sequence matching, EEG/ECG data analysis or prediction of abnormal situation or trend of stock price. In order to efficiently perform similarity search for financial MTS datasets, we present a distance-based index structure (Dbis) for range...
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