Anomaly detection in streaming nonstationary temporal data
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Cited by:
- Priyanga Dilini Talagala & Rob J Hyndman & Catherine Leigh & Kerrie Mengersen & Kate Smith-Miles, 2019. "A Feature-Based Framework for Detecting Technical Outliers in Water-Quality Data from In Situ Sensors," Monash Econometrics and Business Statistics Working Papers 1/19, Monash University, Department of Econometrics and Business Statistics.
- Miguel Flores & Salvador Naya & Rubén Fernández-Casal & Sonia Zaragoza & Paula Raña & Javier Tarrío-Saavedra, 2020. "Constructing a Control Chart Using Functional Data," Mathematics, MDPI, vol. 8(1), pages 1-26, January.
- Sevvandi Kandanaarachchi & Mario A Munoz & Rob J Hyndman & Kate Smith-Miles, 2018. "On normalization and algorithm selection for unsupervised outlier detection," Monash Econometrics and Business Statistics Working Papers 16/18, Monash University, Department of Econometrics and Business Statistics.
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More about this item
Keywords
concept drift; extreme value theory; feature-based time series analysis; kernel-based density estimation; multivariate time series; outlier detection.;All these keywords.
JEL classification:
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ETS-2018-04-09 (Econometric Time Series)
- NEP-FOR-2018-04-09 (Forecasting)
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