Modelling and Analysis on Noisy Financial Time Series

Building the prediction model(s) from the historical time series has attracted many researchers in last few decades. For example, the traders of hedge funds and experts in agriculture are demanding the precise models to make the prediction of the possible trends and cycles. Even though many statistical or machine learning (ML) models have been proposed, however, there are no universal solutions available to resolve such particular prob-lem. In this paper, the powerful forward-backward non-linear filter and wavelet-based denoising method are introduced to remove the high level of noise embedded in financial time series. With the filtered time series, the statistical model known as autoregression is utilized to model the historical times aeries and make the prediction. The proposed models and approaches have been evaluated using the sample time series, and the experimental results have proved that the proposed approaches are able to make the precise prediction very efficiently and effectively

A Novel Subspace Outlier Detection Approach in High Dimensional Data Sets

Many real applications are required to detect outliers in high dimensional data sets. The major difficulty of mining outliers lies on the fact that outliers are often embedded in subspaces. No efficient methods are available in general for subspace-based outlier detection. Most existing subspacebased outlier detection methods identify outliers by searching for abnormal sparse density units in subspaces. In this paper, we present a novel approach for finding outliers in the ‘interesting’ subspaces. The interesting subspaces are strongly correlated with `good\u27 clusters. This approach aims to group the meaningful subspaces and then identify outliers in the projected subspaces. In doing so, an extension to the subspacebased clustering algorithm is proposed so as to find the ‘good’ subspaces, and then outliers are identified in the projected subspaces using some classical outlier detection techniques such as distance-based and density-based algorithms. Comprehensive case studies are conducted using various types of subspace clustering and outlier detection algorithms. The experimental results demonstrate that the proposed method can detect outliers effectively and efficiently in high dimensional data sets

Analysis of Hu\u27s Moment Invariants on Image Scaling and Rotation

Moment invariants have been widely applied to image pattern recognition in a variety of applications due to its invariant features on image translation, scaling and rotation. The moments are strictly invariant for the continuous function. However, in practical applications images are discrete. Consequently, the moment invariants may change over image geometric transformation. To address this research problem, an analysis with respect to the variation of moment invariants on image geometric transformation is presented, so as to analyze the effect of image\u27s scaling and rotation. Finally, the guidance is also provided for minimizing the fluctuation of moment invariants

A new seal verification for Chinese color seal

Automatic seal imprint identification system is highly demanded in oriental countries. Even though several seal identification techniques have been proposed, it is seldom to find the papers on the recovery of lost seal imprint strokes caused by superimposition. In this paper, a new seal verification for Chinese color seal is proposed. This approach segments the seal imprint from the input image in terms of the adaptive thresholds. The lost seal imprint strokes are recovered based on the text stroke width that can be detected automatically. In addition, the moment-based seal verification is to compare the reference seal imprint and the recovered one. Experimental results show that the proposed method is able to correctly and efficiently verify the genuine and forgery seal imprint

Text extraction in natural scenes using region-based method

Text in images is a very important clue for image indexing and retrieving. Unfortunately, it is a challenging work to accurately and robustly extract text from a complex background image. In this paper, a novel region-based text extraction method is proposed. In doing so, the candidate text regions are detected by 8-connected objects detection algorithm based on the edge image. Then the non-text regions are filtered out using shape, texture and stroke width rules. Finally, the remaining regions are grouped into text lines. Since stroke width is the intrinsic and particular characteristics of the text, the accuracy of the non-text filter are notably promoted. The improved Stroke Width Transform in the paper is less computing complexities and more accurate. Experimental results on sample ICDAR competition Dataset and our dataset show that the proposed method has the best performance compared with other five methods

An Online Ballistics Imaging System for Firearm Identification

Since the traditional ballistics imaging system is dependent upon the expertise and experience of end-user, an intelligent ballistics imaging system is highly demanded to overcome the drawbacks of traditional techniques. This paper aims to develop a novel ballistics imaging system so as to combine the traditional functions with new features such as the line-scan image module, the characteristics extraction module, and the intelligent image processing module. With the help of these features, the new system can identify firearm more efficiently and effectively than the traditional techniques

Construction of Bivariate Nonseparable Compactly Supported Orthogonal Wavelets

A method for constructing bivariate nonseparable compactly supported orthogonal scaling functions, and the corresponding wavelets, using the dilation matrixM:=2n=2n[1001],(d=detM=22n≥4,n∈ℕ)is given. The accuracy and smoothness of the scaling functions are studied, thus showing that they have the same accuracy order as the univariate Daubechies low-pass filterm0(ω), to be used in our method. There follows that the wavelets can be made arbitrarily smooth by properly choosing the accuracy parameterr