942 research outputs found
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
Accurate traffic forecasting at intersections governed by intelligent traffic
signals is critical for the advancement of an effective intelligent traffic
signal control system. However, due to the irregular traffic time series
produced by intelligent intersections, the traffic forecasting task becomes
much more intractable and imposes three major new challenges: 1) asynchronous
spatial dependency, 2) irregular temporal dependency among traffic data, and 3)
variable-length sequence to be predicted, which severely impede the performance
of current traffic forecasting methods. To this end, we propose an Asynchronous
Spatio-tEmporal graph convolutional nEtwoRk (ASeer) to predict the traffic
states of the lanes entering intelligent intersections in a future time window.
Specifically, by linking lanes via a traffic diffusion graph, we first propose
an Asynchronous Graph Diffusion Network to model the asynchronous spatial
dependency between the time-misaligned traffic state measurements of lanes.
After that, to capture the temporal dependency within irregular traffic state
sequence, a learnable personalized time encoding is devised to embed the
continuous time for each lane. Then we propose a Transformable Time-aware
Convolution Network that learns meta-filters to derive time-aware convolution
filters with transformable filter sizes for efficient temporal convolution on
the irregular sequence. Furthermore, a Semi-Autoregressive Prediction Network
consisting of a state evolution unit and a semiautoregressive predictor is
designed to effectively and efficiently predict variable-length traffic state
sequences. Extensive experiments on two real-world datasets demonstrate the
effectiveness of ASeer in six metrics
Towards Better Forecasting by Fusing Near and Distant Future Visions
Multivariate time series forecasting is an important yet challenging problem
in machine learning. Most existing approaches only forecast the series value of
one future moment, ignoring the interactions between predictions of future
moments with different temporal distance. Such a deficiency probably prevents
the model from getting enough information about the future, thus limiting the
forecasting accuracy. To address this problem, we propose Multi-Level Construal
Neural Network (MLCNN), a novel multi-task deep learning framework. Inspired by
the Construal Level Theory of psychology, this model aims to improve the
predictive performance by fusing forecasting information (i.e., future visions)
of different future time. We first use the Convolution Neural Network to
extract multi-level abstract representations of the raw data for near and
distant future predictions. We then model the interplay between multiple
predictive tasks and fuse their future visions through a modified
Encoder-Decoder architecture. Finally, we combine traditional Autoregression
model with the neural network to solve the scale insensitive problem.
Experiments on three real-world datasets show that our method achieves
statistically significant improvements compared to the most state-of-the-art
baseline methods, with average 4.59% reduction on RMSE metric and average 6.87%
reduction on MAE metric.Comment: Accepted by AAAI 202
Deep learning for robust forecasting of hot metal silicon content in a blast furnace
The hot metal silicon content is a key indicator of the thermal state in the blast furnace and it needs to be kept within a pre-defined range in order to ensure efficient operations. Effective monitoring of silicon content is challenging due to the harsh environment in the furnace and irregularly sampled measurements. Data-driven approaches have been proposed in the literature to predict silicon content using process data and overcome the sparsity of silicon content measurements. However, these approaches rely on the selection of hand-crafted features and ad hoc interpolation methods to deal with irregular sampling of the process variables, adding complexity to model training and optimisation, and requiring significant effort when tuning the model over time to keep it to the required level of accuracy. This paper proposes an improved framework for the prediction of silicon content using a novel deep learning approach based on Phased LSTM. The model has been trained using 3 years of data and validated over a 1-year period using a robust walk-forward validation method, therefore providing confidence in the model performance over time. The Phased LSTM model outperforms competing approaches due to its in-built ability to learn from event-based sequences and scalability for real-world deployments. This is the first time that Phased LSTM has been applied to real-world datasets and results suggest that the ability to learn from event-based data can be beneficial for the process industry where event-driven signals from multiple sensors are common
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