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In the last few years, the number of R packages implementing different robust statistical methods have increased substantially. There are now numerous packages for computing robust multivariate location and scatter, robust multivariate analysis ...

Multivariate outlier detection in high dimensions with the R package rrcov: Minimum Regularized Covariance Determinant (MRCD) left and robust PCA (ROBPCA) right. image image

Time series anomaly detection is a complex task, often hindered by the limitations of existing deep learning methods in capturing temporal contexts and accurately representing normal patterns, which impacts the effectiveness of anomaly detection. To ...

Multivariate time series forecasting is a critical task with applications across various domains, including finance, energy demand, and climate modeling. This review paper, provides a comprehensive overview of methodologies and advancements in ...

This paper aims to know how well Bidirectional Long Short-Term Memory (BiLSTM) is in predicting Indonesian stock prices. First, the best hyperparameter of BiLSTM is searched through hyperparameter tuning. After finding the best hyperparameter, we train ...

Matrix visualizations are widely used to display large-scale network, tabular, set, or sequential data. They typically only encode a single value per cell, e.g., through color. However, this can greatly limit the visualizations’ utility when exploring ...

In technical systems the analysis of similar load situations is a promising technique to gain information about the system’s state, its health or wearing. Very often, load situations are challenging to be defined by hand. Hence, these situations need to ...

Multivariate Transductive Neuro-Fuzzy Inference System model, named the mTNFI is a previously proposed conceptual transductive approach designed for analysis and modelling of multivariate time-series data. In this study, we revisit, implement and ...

In order to overcome the problems of poor accuracy and high data redundancy in the current big data analysis platform, this paper proposes and designs a multivariable big data mobile analysis platform based on collaborative filtering recommendation ...

We reconsider the multivariate Kyle model in a risk-neutral setting with a single, perfectly informed rational insider and a rational competitive market maker, setting the price of $n$ securities. We prove the unicity of a symmetric, positive definite ...

Techniques of univariate Newton interpolating polynomials are extended to multivariate data points by different generalizations and practical algorithms. The Newton basis format, with divided-difference algorithm for coefficients, generalizes in a ...

Copulas are a powerful tool in multivariate statistics. If copula functions used for modeling dependence between random variables, there is an immediate and obvious need to test whether the model can describe the data at hand accurately enough or not. ...

This survey presents multidimensional scaling (MDS) methods and their applications in real world. MDS is an exploratory and multivariate data analysis technique becoming more and more popular. MDS is one of the multivariate data analysis techniques, ...

A rapid increase in the quantity of data available is allowing all fields of science to generate more accurate models of multivariate phenomena. Regression and interpolation become challenging when the dimension of data is large, especially while ...

Permutation polynomials over finite fields constitute an active research area. Permutation trinomials attract researchers' interest due to their simple algebraic form and some additional extraordinary properties. In this paper, we present six new classes ...

Real time mobile Health applications highly depend on sensor readings to provide high-quality health services. However, real-time sensor readings may be inaccurate and cause abnormal physiological measurements due to internal and external factors. Thus, ...

We present Visualization-by-Sketching, a direct-manipulation user interface for designing new data visualizations. The goals are twofold: First, make the process of creating real, animated, data-driven visualizations of complex information more accessible ...

This paper provides exploratory analysis on gaming statistics via various multivariate and categorical data analysis approaches. The clustering results show that the principal components associated with the gaming data are related to player expertise ...

Discretization of continuous attributes is an important task in rough sets and many discretization algorithms have been proposed. However, most of the current discretization algorithms are univariate, which may reduce the classification ability of a ...

The most common machine learning approach is supervised learning, which uses labeled data for building predictive models. However, in many practical problems, the availability of annotated data is limited due to the expensive, tedious and time-consuming ...

Kalman filter is one of the novel techniques useful for statistical estimation theory and now widely used in many practical applications. In this paper, we consider the process of applying Unscented Kalman Filtering algorithm to multivariate financial ...