Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences

Martínez‐Álvarez, Francisco; Troncoso, Alicia; Riquelme, José C.

doi:10.1007/978-3-642-03915-7_31

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…It can be concluded that there exist few works based on KNN to forecast time series, which have mainly been assessed by means of diverse distance metrics in order to identify univariate time series motifs or episodes in the historical data [91]. …”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

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

et al. 2015

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“…As a case study, the markets of New York, Australia, and the Iberian Peninsula were examined. An early version of this algorithm can be found in [14].…”

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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Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence

Morales‐Esteban

Martínez‐Álvarez

Reyes³

2013

Tectonophysics

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Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences

Cited by 4 publications

References 10 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

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

Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence

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