An extraction method of time-series numerical data from press releases by using co-occurrence conditions of numbers and time stamps related to target business keywords

2010 ◽  
Vol 93 (3) ◽  
pp. 61-69 ◽  
Author(s):  
Masaki Samejima ◽  
Mayu Gen ◽  
Takeshi Nakazaki ◽  
Masanori Akiyoshi ◽  
Norihisa Komoda
Keyword(s):  
Time Series ◽  
Extraction Method ◽  
Numerical Data ◽  
Press Releases ◽  
Time Stamps

scholarly journals On temporal logic constraint solving for analyzing numerical data time series

2008 ◽  
Vol 408 (1) ◽  
pp. 55-65 ◽  
Author(s):  
François Fages ◽  
Aurélien Rizk
Keyword(s):  
Time Series ◽  
Temporal Logic ◽  
Numerical Data ◽  
Constraint Solving

scholarly journals Eagle View: An Abstract Evaluation of Machine Learning Algorithms based on Data Properties

2021 ◽  
Author(s):  
Dhairya Vyas
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Numerical Data ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Almost All

In terms of Machine Learning, the majority of the data can be grouped into four categories: numerical data, category data, time-series data, and text. We use different classifiers for different data properties, such as the Supervised; Unsupervised; and Reinforcement. Each Categorises has classifier we have tested almost all machine learning methods and make analysis among them.

scholarly journals Extracting numerical data from unstructured Arabic texts (ENAT)

2021 ◽  
Vol 21 (3) ◽  
pp. 1759
Author(s):  
Abeer K. AL-Mashhadany ◽  
Dalal N. Hamood ◽  
Ahmed T. Sadiq Al-Obaidi ◽  
Waleed K. Al-Mashhsdany
Keyword(s):  
Text Analysis ◽  
Extraction Method ◽  
Numerical Data ◽  
High Accuracy ◽  
Unstructured Data ◽  
Arithmetic Operations ◽  
Unstructured Text ◽  
Linguistic Rules ◽  
High Level ◽  
Proper Operation

<span id="docs-internal-guid-5dcc170c-7fff-e8e4-10d4-4a07701ca923"><span>Unstructured data becomes challenges because in recent years have observed the ability to gather a massive amount of data from annotated documents. This paper interested with Arabic unstructured text analysis. Manipulating unstructured text and converting it into a form understandable by computer is a high-level aim. An important step to achieve this aim is to understand numerical phrases. This paper aims to extract numerical data from Arabic unstructured text in general. This work attempts to recognize numerical characters phrases, analyze them and then convert them into integer values. The inference engine is based on the Arabic linguistic and morphological rules. The applied method encompasses rules of numerical nouns with Arabic morphological rules, in order to achieve high accurate extraction method. Arithmetic operations are applied to convert the numerical phrase into integer value. The proper operation is determined depending on linguistic and morphological rules. It will be shown that applying Arabic linguistic rules together with arithmetic operations succeeded in extracting numerical data from Arabic unstructured text with high accuracy reaches to 100%.</span></span>

scholarly journals Locality-Based Visual Outlier Detection Algorithm for Time Series

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Zhihua Li ◽  
Ziyuan Li ◽  
Ning Yu ◽  
Steven Wen
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Extraction Method ◽  
Time Series Data ◽  
Data Extraction ◽  
Detection Algorithm ◽  
Series Data ◽  
Practical Application ◽  
Detection Model ◽  
Efficient Data

Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.

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