scholarly journals PREDIKSI JUMLAH KEBERANGKATAN PENUMPANG PESAWAT TERBANG MENGGUNAKAN MODEL VARIASI KALENDER DAN DETEKSI OUTLIER (Studi Kasus di Bandara Soekarno-Hatta)

2020 ◽  
Vol 9 (3) ◽  
pp. 336-345
Author(s):  
Alvi Waldira ◽  
Abdul Hoyyi ◽  
Dwi Ispriyanti
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Outlier Detection ◽  
Air Transportation ◽  
Time Variable ◽  
Unexpected Events ◽  
Transportation Services ◽  
Dummy Variable ◽  
Strategic Role ◽  
Detection Of Outliers

 Transportation has a strategic role, even becoming one of the main needs of the community, especially air transportation services. A large number of passengers in air transportation always experiences a difference every month. One of the differences occurred when approaching Eid al-Fitr, which changes every year based on an Islamic calendar that is different from Masehi calendar. The lunar shift in the occurrence of Eid al-Fitr forms a pattern called calendar variation. The effects of calendar variations can be overcome by using an additional variable, such as a dummy variable, this variable which will be used in the ARIMAX model. Observation of time series is often influenced by several unexpected events such as outliers. This outlier causes the results of data analysis to be less valid. So the researchers added the detection of outliers in this study. Based on the analysis results, the ARIMA calendar variation model is obtained (1.0, [12]), with time variable t, dummy variable , and the addition of one outlier. This model has a MAPE value of 0.07079609 which means this model is very good for forecasting. Forecasting results showed an increase in the number of passengers during the two months before Eid. Keywords: Passenger, calendar variation, outlier detection

scholarly journals A Review on Outlier/Anomaly Detection in Time Series Data

2021 ◽  
Vol 54 (3) ◽  
pp. 1-33
Author(s):  
Ane Blázquez-García ◽  
Angel Conde ◽  
Usue Mori ◽  
Jose A. Lozano
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Time Series Data ◽  
State Of The Art ◽  
Series Data ◽  
Detection Techniques ◽  
The Past ◽  
Time Series Mining ◽  
Detection Of Outliers ◽  
Unsupervised Outlier Detection

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on unsupervised outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.

scholarly journals Extended isolation forest – application to outlier detection in geomagnetic data

2021 ◽  
Vol 929 (1) ◽  
pp. 012022
Author(s):  
S A Imashev
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Geomagnetic Field ◽  
Daily Variation ◽  
Sampling Rate ◽  
Geomagnetic Data ◽  
Detection Of Outliers ◽  
Isolation Forest

Abstract The aim of this study is to present a method for detection of outliers in the time series of total intensity of geomagnetic field using Extended Isolation Forest algorithm. The method is consisted of three steps: 1) generation of additional features that take into account the regular daily variation and smooth behaviour of normal data, 2) detection of potential outliers based on ensemble of extended isolating trees and 3) subsequent refinement based on difference between the outlier and its replacement with interpolated value. Application of the method for detection of outliers in yearly time series of the total geomagnetic field at Ak-Suu and Kegety stations showed that the algorithm identifies both global and contextual outliers. Average classification metrics for the method are characterized as high and have the following values: precision 94.3%, recall 93.9% and F-score 94.5%, and probabilities of errors of the first and second kind are comparable to similar algorithms used for detection of outliers in magnetograms of different sampling rate.

scholarly journals Detection of Outliers through Influence Function on Affinity

2007 ◽  
Vol 6 (2) ◽  
pp. 34-44
Author(s):  
P. Rajalakshmi ◽  
P. Geetha
Keyword(s):  
Data Analysis ◽  
Outlier Detection ◽  
Influence Function ◽  
Data Cleaning ◽  
Pharmaceutical Research ◽  
The Other ◽  
Classification Problems ◽  
Science Data ◽  
Network Intrusion ◽  
Detection Of Outliers

Outliers are the atypical observations that lie at abnormal distances from the other observations in a random sample. Such outliers are often seen as contaminating the data. In general, the rejection of influential outliers improves the accuracy of the estimators and so the results with the identification of outliers have become the most important aspect in any data analysis. Outlier detection finds many applications in the areas such as data cleaning, fraud detection, network intrusion, pharmaceutical research and exploration in science data buses. The distance based outlier detection is the most commonly used method. In this paper, the influence function for affinity is explained and the detection of outliers in classification problems using influence function for affinity is illustrated for univariate data through a few examples.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

AKTUALISASI PEDIDIKAN KARAKTER DALAM PENGEMBANGAN REVOLUSI MENTAL BERBASIS ADIWIYATA DAN MULTIKULTURAL

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Suhardi Suhardi
Keyword(s):  
Data Analysis ◽  
Character Education ◽  
Achievement Motivation ◽  
Case Study Research ◽  
Individual Development ◽  
Human Beings ◽  
Soft Skill ◽  
Design Data ◽  
Strategic Role ◽  
Using Data

Mental revolution of education requires efforts to print educated human beings by having the motivation to meet the standards of achievement excellence, such as ethos of progress, ethics, achievement motivation, discipline, optimistic, productive, innovative and active views. This can be implemented with character education. Character education is one of the soft skill tools that can be integrated in learning in each subject. Learning activities using an active learning approach have a strategic role in instilling national character values so that students are able to behave and act on values that have become their personality. The purpose of this study was to find and analyze about: 1) Implementation of Character Education to Build Adiwiyata-Based Mental Revolution and Multiculturalism; 2) Implementation of Character Education to Build Mental Revolution in Organizational Culture. This study uses a qualitative approach with phenomenological naturatistics (phenomenology approach), with a descriptive type of case study research design. Data were analyzed using data analysis techniques: data reduction, data analysis and conclusions. The results of the study are: The application of character education to develop a mental revolution can be started from the character of building the environment. Environmental character is very important for individual development. The implementation of character education in building a mental revolution can emphasize the internalization of multicultural values and Adiwiyata which in the end will form a loving environmental awareness and foster a spirit of tolerance.

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