Detection of outliers in bivariate time series data

1987 ◽  
Vol 16 (12) ◽  
pp. 3701-3714 ◽  
Author(s):  
Ravindra Khattree ◽  
Dayanand N. Naik
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Detection Of Outliers ◽  
Bivariate Time Series

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.

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

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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