Industrial Time Series Data Cleaning Using Generative LSTM and Adaptive Confidence Interval

Near real-time monitoring of abrupt changes in satellite time series is important for timely warning of land covers changes. Regression model-based method has been frequently used to detect abrupt change (outlier or anomaly) in time series data. Abrupt change is often determined by residuals test after regression. A simple and widely used residuals test technique is confidence interval (CI), which is often time-independent or constant in many studies. However, satellite time series data is characterized by seasonal variability and periodicity. Although the periodicity could be fitted well by a seasonal-trend regression model, the seasonal variability still remains in the residuals of the regression model. The seasonal variability would lead to less reliable results if abrupt changes are detected by a constant confidence interval (CCI). In order to improve the reliability of abrupt change monitoring in satellite time series, in this paper we develop a criterion namely seasonal confidence interval (SCI) of regression residuals. Experimental evaluations with some simulated and actual satellite time series data demonstrate better performance of the proposed SCI criterion than the CCI criterion for monitoring abrupt changes in satellite time series.

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Time Series Data Cleaning under Multi-Speed Constraints

International Journal of Software and Informatics ◽

10.21655/ijsi.1673-7288.00239 ◽

2021 ◽

Vol 11 (1) ◽

pp. 29-54

Author(s):

Fei Gao ◽

◽

Shaoxu Song ◽

Jianmin Wang

Keyword(s):

Time Series ◽

Time Series Data ◽

Data Cleaning ◽

Series Data

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Time series data cleaning

Proceedings of the VLDB Endowment ◽

10.14778/3115404.3115410 ◽

2017 ◽

Vol 10 (10) ◽

pp. 1046-1057 ◽

Cited By ~ 27

Author(s):

Aoqian Zhang ◽

Shaoxu Song ◽

Jianmin Wang ◽

Philip S. Yu

Keyword(s):

Time Series ◽

Time Series Data ◽

Data Cleaning ◽

Series Data

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Time Series Data Cleaning: A Survey

IEEE Access ◽

10.1109/access.2019.2962152 ◽

2020 ◽

Vol 8 ◽

pp. 1866-1881 ◽

Cited By ~ 1

Author(s):

Xi Wang ◽

Chen Wang

Keyword(s):

Time Series ◽

Time Series Data ◽

Data Cleaning ◽

Series Data

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Time Series Data Cleaning Based on Dynamic Speed Constraints

Web Information Systems Engineering – WISE 2020 - Lecture Notes in Computer Science ◽

10.1007/978-3-030-62008-0_33 ◽

2020 ◽

pp. 475-487

Author(s):

Guohui Ding ◽

Chenyang Li ◽

Ru Wei ◽

Shasha Sun ◽

Zhaoyu Liu ◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Data Cleaning ◽

Series Data

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

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

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

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

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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