Multivariate Time Series Forecasting with Dilated Residual Convolutional Neural Networks for Urban Air Quality Prediction

Benhaddi, Meriem; Ouarzazi, Jamal

doi:10.1007/s13369-020-05109-x

Cited by 42 publications

(22 citation statements)

References 34 publications

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“…For example, LSTM [20] and GRU [21] show their strengths in extracting the long-and short-term dependencies, LSTNet [1] combines the CNN and RNN to capture temporal dependencies in the time-series data, DeepAR [9] utilizes the autoregressive model, as well as the RNN, to model the distribution of future time-series. There are also some works focusing on CNN models [22]- [25], which can capture inner patterns of the time-series data through convolution.…”

Section: Related Work a Methods For Time-series Forecastingmentioning

confidence: 99%

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

Li¹,

Lu²,

Xiong³

et al. 2023

Preprint

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Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. Though one could lower the complexity of Transformers by inducing the sparsity in point-wise self-attentions for LTTF, the limited information utilization prohibits the model from exploring the complex dependencies comprehensively. To this end, we propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects: (i) an encoderdecoder architecture incorporating a linear complexity without sacrificing information utilization is proposed on top of slidingwindow attention and Stationary and Instant Recurrent Network (SIRN); (ii) a module derived from the normalizing flow is devised to further improve the information utilization by inferring the outputs with the latent variables in SIRN directly; (iii) the inter-series correlation and temporal dynamics in time-series data are modeled explicitly to fuel the downstream self-attention mechanism. Extensive experiments on seven real-world datasets demonstrate that Conformer outperforms the state-of-the-art methods on LTTF and generates reliable prediction results with uncertainty quantification.

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Section: Related Work a Methods For Time-series Forecastingmentioning

confidence: 99%

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

Li¹,

Lu²,

Xiong³

et al. 2023

Preprint

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show abstract

“…So far, on the definition of HR strategy, scholars have different understandings and different emphasis, some focus on planning, and some focus on strategic positioning, but their understandings have their commonality, they have the same understanding of the essence of HR, and they believe that HR strategy has three levels of meaning; namely, firstly, the formulation of HR strategy must be based on basic judgments; secondly HR strategy has a guiding significance to the direction of HRM; third is that HR strategy should be formulated following its guiding ideology, clarify the main objective of the strategy, and develop corresponding measures to achieve this main objective [8]. Regarding the research on the method of HR strategy decision, most of the scholars' researches focus on different aspects of HRM, such as salary decision and planning decision, while there are fewer researches on the decision of overall HR strategy and the method of selecting an HR strategy, and most of the proposed strategic decision methods are also subjective and lacking in quantitative methods [9].…”

Section: Related Workmentioning

confidence: 99%

Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network

Zhang

et al. 2022

Journal of Mathematics

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In this paper, RNN (Recurrent Neural Network) algorithm is used to conduct an in-depth analysis of HR strategic decision-making and an HR strategic decision model is constructed for simulation. The four evaluation dimensions of index screening are extracted, the generalized gray correlation analysis is applied to screen the initial selection indexes of HR strategic decision-making, and then the HR strategic decision-making index system of power supply enterprises is constructed. On this basis, the applicability of the BP neural network in HR strategic decision-making is analysed and demonstrated, a BP neural network-based HR strategic decision-making model for power supply enterprises is constructed, and the rationality of the model is illustrated through the model training and testing. Finally, an empirical study is conducted with S power supply company as an example to illustrate the operability of the BP network model for HR strategic decision-making. The results of this paper provide a scientific basis for human resource decision-making in power supply enterprises and provide theoretical support for promoting the healthy development of power supply enterprises. Improving the level of human resource management can make fuller use of human resources and realize the strategic goals of the enterprise. A strategy for optimizing the training of hybrid convolutional neural networks is proposed, using an exponential linear unit activation function to solve the problem of neuron dead zones, a hybrid pooling strategy is used to improve the problem of feature information loss in maximum pooling processing, and an improved cross-entropy loss function is used to solve the problem of insufficient learning of difficult classification samples. The optimization of the model training process is finally completed, and the recommendation quality is improved. The Bayesian probability distribution table is learned to be filled in the expert data set; finally, the accuracy and effectiveness of the model are tested on the experimental platform. The results of the experiments show that the established Bayesian model beats the platform’s built-in intelligent method with a 78.2% win rate. That is, the model can make intelligent recommendations for strategies in staffed and unmanned platform decision-making and command-and-control combat units to execute tactical actions to achieve the best operational effectiveness.

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“…Ke Gu [7] proposed a heuristic recurrent air quality predictor (RAQP) to exploit the meteorological factors and air pollutant concentration data which have strong influences on air quality of the next adjacent moment. Meriem Benhaddi [8] built a WaveNet architecture to forecast the conditional multivariate time series data. The architecture is composed of stacked residual convolutions which used parameterized skip connections to catch early trends in a large scope in the time series history.…”

Section: B Prediction Based On Time Series Modelmentioning

confidence: 99%

“…Air quality data has been widely concerned in the world. Time series data prediction method, like traditional machine learning methods [1][2][3][4][5] and time series prediction models [6][7][8], is often used for air quality prediction. However, the existing air quality prediction methods cannot effectively capture the complex nonlinearity of air quality, like PM2.5 concentrations.…”

Section: Introductionmentioning

confidence: 99%

Air Quality Prediction Based on Integrated Dual LSTM Model

Chen

Guan

2021

IEEE Access

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Air quality prediction is an important reference for meteorological forecast and air controlling, but over fitting often occurs in prediction algorithms based on a single model. Aiming at the complexity of air quality prediction, a prediction method based on integrated dual LSTM (Long Short-Term Memory) model was proposed in this paper. Firstly, the Seq2Seq (Sequence to Sequence) technology is used to establish a single-factor prediction model which can obtain the predicted value of each component in air quality data, independently. Each component of air quality is regarded as time series data in the forecasting process. Then, the LSTM model with attention mechanism is used as the multi-factor prediction model. The influencing factors of air quality, like the data of neighboring stations and weather data, are considered in the model. Finally, XGBoosting (eXtreme Gradient Boosting) tree is used to integrate two models. The final prediction results can be obtained by accumulating the predicted values of the optimal subtree nodes. Through evaluation and analysis using five evaluation methods, the proposed method has better performance in terms of error and model expression power. Compared with other various models, the precision of prediction data has been greatly improved in our model.

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Multivariate Time Series Forecasting with Dilated Residual Convolutional Neural Networks for Urban Air Quality Prediction

Cited by 42 publications

References 34 publications

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network

Air Quality Prediction Based on Integrated Dual LSTM Model

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