Robust exponential smoothing of multivariate time series

Croux, Christophe; Gelper, Sarah; Mahieu, Koen

doi:10.1016/j.csda.2009.05.003

Cited by 31 publications

(12 citation statements)

References 17 publications

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“…Note the similarity with the predicting equation for a multivariate exponential smoothing model (see for example Eq. (1) in Croux et al (2010)). Observe…”

Section: Remarkmentioning

confidence: 94%

“…Boudt and Croux (2010) extended the results of Muler and Yohai (2008) to vector GARCH models. Croux et al (2010) proposed a robust method for estimating the parameters of multivariate exponential smoothing models based on data cleaning. However, there is no proposal for VARMA models aimed at reducing the propagation of the effect of outliers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust estimation for vector autoregressive models

Muler

Yohai

2013

Computational Statistics & Data Analysis

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“…Note the similarity with the predicting equation for a multivariate exponential smoothing model (see for example Eq. (1) in Croux et al (2010)). Observe…”

Section: Remarkmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Robust estimation for vector autoregressive models

Muler

Yohai

2013

Computational Statistics & Data Analysis

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“…Gelper et al proposed an adapted version of the classical exponential and Holt-Winters smoothing methodologies, providing them with robustness . Another version of a robust multivariate exponential smoothing applied to time series can be found in Croux et al (2010). Following with classical methods, a work that enhanced ARMA by adding robustness can be found in Muler et al (2009), in which the authors succeeded in limiting the effect of outlying data to the time stamp in which they happen.…”

Section: Robust Statistical Methods To Detect Outliersmentioning

confidence: 99%

Discovery of motifs to forecast outlier occurrence in time series

Martínez‐Álvarez

Troncoso

Riquelme

et al. 2011

Pattern Recognition Letters

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a b s t r a c tThe forecasting process of real-world time series has to deal with especially unexpected values, commonly known as outliers. Outliers in time series can lead to unreliable modeling and poor forecasts. Therefore, the identification of future outlier occurrence is an essential task in time series analysis to reduce the average forecasting error. The main goal of this work is to predict the occurrence of outliers in time series, based on the discovery of motifs. In this sense, motifs will be those pattern sequences preceding certain data marked as anomalous by the proposed metaheuristic in a training set. Once the motifs are discovered, if data to be predicted are preceded by any of them, such data are identified as outliers, and treated separately from the rest of regular data. The forecasting of outlier occurrence has been added as an additional step in an existing time series forecasting algorithm (PSF), which was based on pattern sequence similarities. Robust statistical methods have been used to evaluate the accuracy of the proposed approach regarding the forecasting of both occurrence of outliers and their corresponding values. Finally, the methodology has been tested on six electricity-related time series, in which most of the outliers were properly found and forecasted.

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“…Gelper et al proposed an adapted version of the classical exponential and Holt-Winters smoothing methodologies, providing them with robustness [7]. Another version of a robust multivariate exponential smoothing applied to time series can be found in [8]. Following classical methods, a work that enhanced Auto Regressive Moving Average (ARMA) by adding robustness can be found in [9], in which the authors succeeded in limiting the effect of outlying data to the time stamp in which they happen.…”

Section: Related Workmentioning

confidence: 99%

A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series

et al. 2016

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Abstract:The occurrence of outliers in real-world phenomena is quite usual. If these anomalous data are not properly treated, unreliable models can be generated. Many approaches in the literature are focused on a posteriori detection of outliers. However, a new methodology to a priori predict the occurrence of such data is proposed here. Thus, the main goal of this work is to predict the occurrence of outliers in time series, by using, for the first time, imbalanced classification techniques. In this sense, the problem of forecasting outlying data has been transformed into a binary classification problem, in which the positive class represents the occurrence of outliers. Given that the number of outliers is much lower than the number of common values, the resultant classification problem is imbalanced. To create training and test sets, robust statistical methods have been used to detect outliers in both sets. Once the outliers have been detected, the instances of the dataset are labeled accordingly. Namely, if any of the samples composing the next instance are detected as an outlier, the label is set to one. As a study case, the methodology has been tested on electricity demand time series in the Spanish electricity market, in which most of the outliers were properly forecast.

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Robust exponential smoothing of multivariate time series

Cited by 31 publications

References 17 publications

Robust estimation for vector autoregressive models

Robust estimation for vector autoregressive models

Discovery of motifs to forecast outlier occurrence in time series

A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series

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