Resampling strategies for imbalanced time series forecasting

Moniz, Nuno; Branco, Paula; Torgo, Luı́s

doi:10.1007/s41060-017-0044-3

Cited by 45 publications

(25 citation statements)

References 38 publications

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“…Therefore, it is expected that prediction techniques may fail to correctly capture the upstroke phase in such cases and thus may produce a poor forecast overall. A common approach for tackling such issues is the use of resampling strategies (Moniz et al, 2017 ), which operate on the training dataset to make the distribution of the data points more balanced in terms of their information content.…”

Section: Methodsmentioning

confidence: 99%

Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing

et al. 2021

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The electrical signals triggering the heart's contraction are governed by non-linear processes that can produce complex irregular activity, especially during or preceding the onset of cardiac arrhythmias. Forecasts of cardiac voltage time series in such conditions could allow new opportunities for intervention and control but would require efficient computation of highly accurate predictions. Although machine-learning (ML) approaches hold promise for delivering such results, non-linear time-series forecasting poses significant challenges. In this manuscript, we study the performance of two recurrent neural network (RNN) approaches along with echo state networks (ESNs) from the reservoir computing (RC) paradigm in predicting cardiac voltage data in terms of accuracy, efficiency, and robustness. We show that these ML time-series prediction methods can forecast synthetic and experimental cardiac action potentials for at least 15–20 beats with a high degree of accuracy, with ESNs typically two orders of magnitude faster than RNN approaches for the same network size.

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Section: Methodsmentioning

confidence: 99%

Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing

et al. 2021

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“…Resampling is conducted for two purposes in this research: ensemble methods (discussed in Sect. 3.2) use repeated resampling to generate diversity among ensemble members (Brown et al, 2005) and as a preprocessing technique to change the training data distribution to influence model performance across the target domain (Moniz et al, 2017a). This following sections discusses the use of resampling as a preprocessing technique.…”

Section: Resampling Techniquesmentioning

confidence: 99%

“…Thus, ensemble methods are often combined with preprocessing strategies to address the imbalance problem (Galar et al, 2012). Resampling is a common preprocessing technique that can be used to create more uniformly distributed target dataset or generate synthetic data with which to train models (Moniz et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

“…CC BY 4.0 License. The utilisation of a fixed threshold for distinguishing between common and rare samples is used both in flow forecasting (Crochemore et al, 2015;Razali et al, 2020;Fleming et al, 2015) and in more general machine learning studies that are focused on the imbalance problem (Moniz et al, 2017a). In this research, the high flow threshold is simply and arbitrarily taken as the 80th percentile value of the observed flow.…”

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Resampling and ensemble techniques for improving ANN-based high streamflow forecast accuracy

Snieder¹,

Abogadil²,

Khan³

2020

Preprint

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Abstract. Data-driven flow forecasting models, such as Artificial Neural Networks (ANNs), are increasingly used for operational flood warning systems. However, flow distributions are highly imbalanced, resulting in poor prediction accuracy on high flows, both in terms of amplitude and timing error. Resampling and ensemble techniques have shown to improve model performance of imbalanced datasets such as streamflow. In this research, we systematically evaluate and compare three resampling: random undersampling (RUS), random oversampling (ROS), and SMOTER; and four ensemble techniques: randomised weights and biases, bagging, adaptive boosting (AdaBoost), least squares boosting (LSBoost); on their ability to improve high flow prediction accuracy using ANNs. The methods are implemented both independently and in combined, hybrid techniques. While some of these combinations have been explored in the broader machine learning literature, this research contains many of the first instances of these algorithms to address the imbalance problem inherent in flood and high flow forecasting models. Specifically, the implementation of ROS, and new approaches for SMOTER, LSBOOST, and SMOTER-AdaBoost are presented in this research. Data from two Canadian watersheds (the Bow River in Alberta, and the Don River in Ontario), representing distinct hydrological systems, are used as the basis for the comparison of the methods. The models are evaluated on overall performance and on high flows. The results of this research indicate that resampling produces marginal improvements to high flow prediction accuracy, whereas ensemble methods produce more substantial improvements, with or without a resampling method. Compared to simple ANN flow forecast models, the use of ensemble methods is recommended to reduce the amplitude and timing error in highly imbalanced flow datasets.

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“…Hence, over-sampling [55] and under-sampling [56] techniques are usually used to balance the number of instances of both classes. However, applying these techniques to time series is challenging, particularly in cases where new time series must be artificially generated [57,58]. The difficulty increases in the multi-time series domain as values of a given time series might be affected by the others.…”

Section: Introductionmentioning

confidence: 99%

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study

Canizo¹,

Triguero

Conde³

et al. 2019

Neurocomputing

289

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Detecting anomalies in time series data is becoming mainstream in a wide variety of industrial applications in which sensors monitor expensive machinery. The complexity of this task increases when multiple heterogeneous sensors provide information of different nature, scales and frequencies from the same machine. Traditionally, machine learning techniques require a separate data preprocessing before training, which tends to be very time-consuming and often requires domain knowledge. Recent deep learning approaches have shown to perform well on raw time series data, eliminating the need for pre-processing. In this work, we propose a deep learning based approach for supervised multitime series anomaly detection that combines a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) in different ways. Unlike other approaches, we use independent CNNs, so-called convolutional heads, to deal with anomaly detection in multi-sensor systems. We address each sensor individually avoiding the need for data pre-processing and allowing for a more tailored architecture for each type of sensor. We refer to this architecture as Multi-head CNN-RNN. The proposed architecture is assessed against a real industrial case study, provided by an industrial partner, where a service elevator is monitored. Within this case study, three type of anomalies are considered: point, context-specific, and collective. The experimental results show that the proposed architecture is suitable for multi-time series anomaly detection as it obtained promising results on the real industrial scenario.

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Resampling strategies for imbalanced time series forecasting

Cited by 45 publications

References 38 publications

Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing

Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing

Resampling and ensemble techniques for improving ANN-based high streamflow forecast accuracy

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study

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