Discovery of motifs to forecast outlier occurrence in time series

Martínez‐Álvarez, Francisco; Troncoso, Alicia; Riquelme, José C.; Aguilar–Ruiz, Jesús S.

doi:10.1016/j.patrec.2011.05.002

Cited by 23 publications

(10 citation statements)

References 42 publications

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“…Finally, an extended and improved approach, PSF, was introduced in [119], where New York, Australian and Spanish electricity and demand time series were successfully forecasted, showing remarkable performance compared to classical methods. The same method was adapted to forecast outliers (o-PSF) for the same markets in [120].…”

Section: Other Modelsmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.

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Section: Other Modelsmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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“…denoted by η ave , and η=γ·η ave , where γ is the coefficient and γ (1,2). The advantages of this outlier detection method can be illuminated from two aspects: First, there is a uniform structure between change detection and outlier detection, which make the detective procedure simple; Second, the formulae for outlier detection are the parts of the formulae for change detection, and this approach further reduces the computational complexity.…”

Section: Outlier Detection Methodsmentioning

confidence: 99%

“…Identification of outliers in a data stream has been one of the most exciting topics in data mining [1][2] and statistics [3], and the issue of detecting change points in time-series data has also extensively been addressed in statistics [4]. The presence of those specific points could easily mislead the conventional time series analysis procedure to the erroneous conclusions.…”

Section: Introductionmentioning

confidence: 99%

On-line outlier and change point detection for time series

Zhu

Liu

et al. 2013

J. Cent. South Univ.

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Abstract:The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detection, etc. In most previous works, outlier detection and change point detection have not been related explicitly and the change point detections did not consider the influence of outliers, in this work, a unified detection framework was presented to deal with both of them. The framework is based on ALARCON-AQUINO and BARRIA's change points detection method and adopts two-stage detection to divide the outliers and change points. The advantages of it lie in that: firstly, unified structure for change detection and outlier detection further reduces the computational complexity and make the detective procedure simple; Secondly, the detection strategy of outlier detection before change point detection avoids the influence of outliers to the change point detection, and thus improves the accuracy of the change point detection. The simulation experiments of the proposed method for both model data and actual application data have been made and gotten 100% detection accuracy. The comparisons between traditional detection method and the proposed method further demonstrate that the unified detection structure is more accurate when the time series are contaminated by outliers.

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“…Since its first publication, several authors have proposed modifications for its improvement. In [24], the authors modified PSF to forecast outliers in time series. Later, Fujimoto et al [25] chose a different clustering method, in order to find clusters with different shapes.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand

et al. 2018

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The forecasting of future values is a very challenging task. In almost all scientific disciplines, the analysis of time series provides useful information and even economic benefits. In this context, this paper proposes a novel hybrid algorithm to forecast functional time series with arbitrary prediction horizons. It integrates a well-known clustering functional data algorithm into a forecasting strategy based on pattern sequence similarity, which was originally developed for discrete time series. The new approach assumes that some patterns are repeated over time, and it attempts to discover them and evaluate their immediate future. Hence, the algorithm first applies a clustering functional time series algorithm, i.e., it assigns labels to every data unit (it may represent either one hour, or one day, or any arbitrary length). As a result, the time series is transformed into a sequence of labels. Later, it retrieves the sequence of labels occurring just after the sample that we want to be forecasted. This sequence is searched for within the historical data, and every time it is found, the sample immediately after is stored. Once the searching process is terminated, the output is generated by weighting all stored data. The performance of the approach has been tested on real-world datasets related to electricity demand and compared to other existing methods, reporting very promising results. Finally, a statistical significance test has been carried out to confirm the suitability of the election of the compared methods. In conclusion, a novel algorithm to forecast functional time series is proposed with very satisfactory results when assessed in the context of electricity demand.

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Discovery of motifs to forecast outlier occurrence in time series

Cited by 23 publications

References 42 publications

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

On-line outlier and change point detection for time series

A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand

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