Multi-output bus travel time prediction with convolutional LSTM neural network

Petersen, Niklas; Rodrigues, Filipe; Pereira, Francisco C.

doi:10.1016/j.eswa.2018.11.028

Cited by 232 publications

(130 citation statements)

References 24 publications

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“…There exist several strategies to deal with multistep forecasting problems [32]: the recursive strategy, which performs one-step predictions and feeds the result as the last input for the next prediction; the direct strategy, which builds one model for each time step; and the multi-output approach, which outputs the complete forecasting horizon vector using just one model. As suggested in recent forecasting studies that use neural networks [33,34], in this work, we adopt the MIMO strategy (Multi-Input Multi-Output) which belongs to the last category. Instead of forecasting each time-step independently, the MIMO approach can model the dependencies between the predicted values since it outputs the complete forecasting window.…”

Section: Data Preprocessingmentioning

confidence: 99%

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

et al. 2020

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Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

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Section: Data Preprocessingmentioning

confidence: 99%

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

et al. 2020

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“…We expect that discussed tools can be used to obtain theoretical guarantees in the multi-label [28][29][30] and memory-constrained settings (we will explore this research direction in the future). We also consider extensions to different variants of the multi-class classification problem [31,32] and multi-output learning tasks [33,34]. We thus plan to build a unified theoretical framework for understanding extreme classification trees.…”

Section: Discussionmentioning

confidence: 99%

Extreme Multiclass Classification Criteria

Choromanska

Jain

2019

Computation

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We analyze the theoretical properties of the recently proposed objective function for efficient online construction and training of multiclass classification trees in the settings where the label space is very large. We show the important properties of this objective and provide a complete proof that maximizing it simultaneously encourages balanced trees and improves the purity of the class distributions at subsequent levels in the tree. We further explore its connection to the three well-known entropy-based decision tree criteria, i.e., Shannon entropy, Gini-entropy and its modified variant, for which efficient optimization strategies are largely unknown in the extreme multiclass setting. We show theoretically that this objective can be viewed as a surrogate function for all of these entropy criteria and that maximizing it indirectly optimizes them as well. We derive boosting guarantees and obtain a closed-form expression for the number of iterations needed to reduce the considered entropy criteria below an arbitrary threshold. The obtained theorem relies on a weak hypothesis assumption that directly depends on the considered objective function. Finally, we prove that optimizing the objective directly reduces the multi-class classification error of the decision tree.

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“…There exist several strategies to deal with multi-step forecasting problems [32]: the recursive strategy that performs one-step predictions and feeds the result as the last input for the next prediction; the direct strategy that builds one model for each time step; and the multi-output approach that outputs the complete forecasting horizon vector using just one model. As suggested in recent forecasting studies that use neural networks [33,34], in this work we adopt the MIMO strategy (Multi-Input Multi-Output) that belongs to the last category. Instead of forecasting each time-step independently, the MIMO approach can model the dependencies between the predicted values since it outputs the complete forecasting window.…”

Section: Data Preprocessingmentioning

confidence: 99%

Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

Lara-Benítez¹,

Carranza-García²,

Luna-Romera³

et al. 2020

Preprint

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Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand, and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

show abstract

Multi-output bus travel time prediction with convolutional LSTM neural network

Cited by 232 publications

References 24 publications

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

Extreme Multiclass Classification Criteria

Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

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