scholarly journals A Data Representation Model for Personalized Medicine

2021 ◽  
Vol 16 (4) ◽  
pp. 0-0
Keyword(s):  
Time Series ◽  
Personalized Medicine ◽  
Series Representation ◽  
Data Representation ◽  
Two Dimensions ◽  
Approximation Technique ◽  
Data Types ◽  
Nominal Data ◽  
Representation Model ◽  
Proposed Model

Personalized medicine exploits the patient data, for example, genetic compositions, and key biomarkers. During the data mining process, the key challenges are the information loss, the data types heterogeneity and the time series representation. In this paper, a novel data representation model for personalized medicine is proposed in light of these challenges. The proposed model will account for the structured, temporal and non-temporal data and their types, namely, numeric, nominal, date, and Boolean. After the "Date and Boolean" data transformation, the nominal data are treated by dispersion while several clustering techniques are deployed to control the numeric data distribution. Ultimately, the transformation process results in three homogeneous representations with these representations having only two dimensions to ease the exploration of the represented dataset. Compared to the Symbolic Aggregate Approximation technique, the proposed model preserves the time-series information, conserves as much data as possible and offers multiple simple representations to be explored.

scholarly journals Scientific Experimental Data Representation Standard through Knowledge Metadata Representation Model

2019 ◽  
Vol 18 (02) ◽  
pp. 1950022
Author(s):  
Nur Adila Azram ◽  
Rodziah Atan
Keyword(s):  
Experimental Data ◽  
Data Structure ◽  
Data Representation ◽  
Representation Model ◽  
Proposed Model ◽  
Scientific Experiments ◽  
Heterogeneous Nature ◽  
Analysis Of Results ◽  
Standard Structure

The growth of data from scientific experiments is increasing nowadays. These data came from different experiments done through various laboratory instruments or machines. It became an issue to manage and analyse scientific experimental data because of the heterogeneous nature of data structure and format. This paper proposed a knowledge metadata representation model to standardise the scientific experimental data representation to make it a standard structure. We discussed the methodology of the proposed model and gives the analysis of results. The evaluation and validation of the knowledge metadata representation model, as well as the verification of the metadata elements extraction, show promising results.

A time series representation model for accurate and fast similarity detection

2009 ◽  
Vol 42 (11) ◽  
pp. 2998-3014 ◽  
Author(s):  
Francesco Gullo ◽  
Giovanni Ponti ◽  
Andrea Tagarelli ◽  
Sergio Greco
Keyword(s):  
Time Series ◽  
Series Representation ◽  
Similarity Detection ◽  
Representation Model

scholarly journals Seasonal Modelling of Fourier Series with Linear Trend

2016 ◽  
Vol 5 (6) ◽  
pp. 65 ◽  
Author(s):  
Iberedem A. Iwok
Keyword(s):  
Time Series ◽  
Fourier Series ◽  
Comparative Study ◽  
Linear Combination ◽  
Linear Function ◽  
Linear Trend ◽  
Series Representation ◽  
Trigonometric Functions ◽  
Proposed Model ◽  
Periodic Time Series

This work was motivated by the need to model a periodic time series function with linear trend. A Fourier series representation with detrended linear function was proposed. In this representation, the time series  is expressed as a combination of the linear trend component and a linear combination of  orthogonal trigonometric functions; where  is the number of seasons. The method was applied to a rainfall data and the proposed model was found to give a good fit. Comparative study was carried out with the complete Fourier representation. Diagnostic checks revealed that the proposed method performs better the pure Fourier approach.

A Smart and Dynamic Decision Support System for Nonlinear Environments

2015 ◽  
pp. 137-161
Author(s):  
S. Uma ◽  
J. Suganthi
Keyword(s):  
Time Series ◽  
Dimensionality Reduction ◽  
Time Series Data ◽  
Series Representation ◽  
Principal Component ◽  
Nonlinear Time Series ◽  
Kernel Principal Component Analysis ◽  
Series Data ◽  
Proposed Model ◽  
Decision Making System

The design of a dynamic and efficient decision-making system for real-world systems is an essential but challenging task since they are nonlinear, chaotic, and high dimensional in nature. Hence, a Support Vector Machine (SVM)-based model is proposed to predict the future event of nonlinear time series environments. This model is a non-parametric model that uses the inherent structure of the data for forecasting. The dimensionality of the data is reduced besides controlling noise as the first preprocessing step using the Hybrid Dimensionality Reduction (HDR) and Extended Hybrid Dimensionality Reduction (EHDR) nonlinear time series representation techniques. It is also used for subsequencing the nonlinear time series data. The proposed SVM-based model using EHDR is compared with the models using Symbolic Aggregate approXimation (SAX), HDR, SVM using Kernel Principal Component Analysis (KPCA), and SVM using varying tube size values for historical data on different financial instruments. A comparison of the experimental results of the proposed model with other models taken for the experimentation has proven that the prediction accuracy of the proposed model is outstanding.

A study of practice based design research models from knowledge integration perspective

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Honghai LI ◽  
Jun CAI
Keyword(s):  
Knowledge Production ◽  
Theoretical Basis ◽  
Knowledge Integration ◽  
Design Research ◽  
Research Process ◽  
Two Dimensions ◽  
Design Knowledge ◽  
Research Projects ◽  
Proposed Model ◽  
Design Innovation

The transformation of China's design innovation industry has highlighted the importance of design research. The design research process in practice can be regarded as the process of knowledge production. The design 3.0 mode based on knowledge production MODE2 has been shown in the Chinese design innovation industry. On this cognition, this paper establishes a map with two dimensions of how knowledge integration occurs in practice based design research, which are the design knowledge transfer and contextual transformation of design knowledge. We use this map to carry out the analysis of design research cases. Through the analysis, we define four typical practice based design research models from the viewpoint of knowledge integration. This method and the proposed model can provide a theoretical basis and a path for better management design research projects.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

Randomized Trees for Time Series Representation and Similarity

2021 ◽  
pp. 108097
Author(s):  
Berk Görgülü ◽  
Mustafa Gökçe Baydoğan
Keyword(s):  
Time Series ◽  
Series Representation

scholarly journals A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 731
Author(s):  
Mengxia Liang ◽  
Xiaolong Wang ◽  
Shaocong Wu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Stock Price ◽  
Multivariate Time Series ◽  
Temporal Features ◽  
Proposed Model ◽  
Time Series Segmentation ◽  
Similarity Model ◽  
Dynamic Time ◽  
Investment Portfolios

Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

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