Short-term traffic volume prediction by ensemble learning in concept drifting environments

Xiao, Jianhua; Xiao, Zhu; Bai, Jian; Havyarimana, Vincent; Zeng, Fanzi

doi:10.1016/j.knosys.2018.10.037

Cited by 52 publications

(24 citation statements)

References 29 publications

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“…Our approach differs from existing techniques in that we do not aim at training a single predictor, but combine multiple predictors. In this respect our approach is close to recent works on ensemble learning, e.g., for road traffic prediction [22]. In particular, it is closest to the bucket of models ensemble technique with gating, but in our scheme the master policy chooses among the predictors in an online fashion.…”

Section: Related Workmentioning

confidence: 58%

A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction

Moayyedi

Dán

et al. 2020

IEEE J. Select. Areas Commun.

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Network traffic prediction is a fundamental prerequisite for dynamic resource provisioning in wireline and wireless networks, but is known to be challenging due to non-stationarity and due to its burstiness and self-similar nature. The prediction of network traffic at the user level is particularly challenging, because the traffic characteristics emerge from a complex interaction of user level and application protocol behavior. In this work we address the problem of predicting the network traffic at the user level over a short horizon, motivated by its applications in cellular scheduling. Motivated by recent works on robust adversarial learning, we treat the prediction problem for non-stationary traffic in an adversarial context, and propose a meta-learning scheme that consists of a set of predictors, each optimized to predict a particular kind of traffic, and of a master policy that is trained for choosing the best fit predictor dynamically based on recent prediction performance, using deep reinforcement learning. We evaluate the proposed meta-learning scheme on a variety of traffic traces consisting of video and nonvideo traffic. Our results show that it consistently outperforms state-of-the-art predictors, and can adapt to before unseen traffic without the need for retraining the individual predictors.

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Section: Related Workmentioning

confidence: 58%

A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction

Moayyedi

Dán

et al. 2020

IEEE J. Select. Areas Commun.

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“…29 Ribeiro and dos Santos 30 analyzed and studied the role of different types of ensemble learning methods (Bagging, Boosting, and Stacking) on predicting price series, and finally concluded that ensemble technique showed statistically significant gains, reducing prediction errors to a large extent. In addition, many recent studies 20,[31][32][33] have analyzed ensemble learning methods and have made great progress in this regard. Thus, ensemble learning based on mode transformation methods was introduced to forecast the carbon price in this study.…”

Section: Overview Of Studies On Carbon Price Forecasting and Other mentioning

confidence: 99%

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Sun

2020

Energy Science & Engineering

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Global climate change, which was triggered by the excessive emissions of greenhouse gases, has become an obstacle in sustainable development. Among all greenhouse gases, carbon dioxide is the most prominent and is artificially controllable. China ranks as the largest emitter of carbon dioxide worldwide and is a pioneer in economic growth. 1 As the international community is advocating in reducing carbon emissions, Chinese carbon emissions and its corresponding

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“…When those methods detect a drift, the switch between the two prediction models is performed. Since EDDM can only be applied to classification problems, we need to transform the regression problem [25]. EIA (see Section 3), in contrast, switches between models based on the EWMA of the prediction errors in the last 6 hours.…”

Section: First Evaluationmentioning

confidence: 99%

Handling Concept Drifts in Regression Problems – the Error Intersection Approach

Baier¹,

Kühl²,

Satzger³

et al. 2020

WI2020 Zentrale Tracks

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Machine learning models are omnipresent for predictions on big data. One challenge of deployed models is the change of the data over time-a phenomenon called concept drift. If not handled correctly, a concept drift can lead to significant mispredictions. We explore a novel approach for concept drift handling, which depicts a strategy to switch between the application of simple and complex machine learning models for regression tasks. We assume that the approach plays out the individual strengths of each model, switching to the simpler model if a drift occurs and switching back to the complex model for typical situations. We instantiate the approach on a real-world data set of taxi demand in New York City, which is prone to multiple drifts, e.g. the weather phenomena of blizzards, resulting in a sudden decrease of taxi demand. We are able to show that our suggested approach outperforms all regarded baselines significantly.

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Short-term traffic volume prediction by ensemble learning in concept drifting environments

Cited by 52 publications

References 29 publications

A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction

A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Handling Concept Drifts in Regression Problems – the Error Intersection Approach

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