33,924 research outputs found
A Study of Deep Learning for Network Traffic Data Forecasting
We present a study of deep learning applied to the domain of network traffic
data forecasting. This is a very important ingredient for network traffic
engineering, e.g., intelligent routing, which can optimize network performance,
especially in large networks. In a nutshell, we wish to predict, in advance,
the bit rate for a transmission, based on low-dimensional connection metadata
("flows") that is available whenever a communication is initiated. Our study
has several genuinely new points: First, it is performed on a large dataset
(~50 million flows), which requires a new training scheme that operates on
successive blocks of data since the whole dataset is too large for in-memory
processing. Additionally, we are the first to propose and perform a more
fine-grained prediction that distinguishes between low, medium and high bit
rates instead of just "mice" and "elephant" flows. Lastly, we apply
state-of-the-art visualization and clustering techniques to flow data and show
that visualizations are insightful despite the heterogeneous and non-metric
nature of the data. We developed a processing pipeline to handle the highly
non-trivial acquisition process and allow for proper data preprocessing to be
able to apply DNNs to network traffic data. We conduct DNN hyper-parameter
optimization as well as feature selection experiments, which clearly show that
fine-grained network traffic forecasting is feasible, and that domain-dependent
data enrichment and augmentation strategies can improve results. An outlook
about the fundamental challenges presented by network traffic analysis (high
data throughput, unbalanced and dynamic classes, changing statistics, outlier
detection) concludes the article.Comment: 16 pages, 12 figures, 28th International Conference on Artificial
Neural Networks (ICANN 2019
Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?
Network-level traffic condition forecasting has been intensively studied for
decades. Although prediction accuracy has been continuously improved with
emerging deep learning models and ever-expanding traffic data, traffic
forecasting still faces many challenges in practice. These challenges include
the robustness of data-driven models, the inherent unpredictability of traffic
dynamics, and whether further improvement of traffic forecasting requires more
sensor data. In this paper, we focus on this latter question and particularly
on data from loop detectors. To answer this, we propose an uncertainty-aware
traffic forecasting framework to explore how many samples of loop data are
truly effective for training forecasting models. Firstly, the model design
combines traffic flow theory with graph neural networks, ensuring the
robustness of prediction and uncertainty quantification. Secondly, evidential
learning is employed to quantify different sources of uncertainty in a single
pass. The estimated uncertainty is used to "distil" the essence of the dataset
that sufficiently covers the information content. Results from a case study of
a highway network around Amsterdam show that, from 2018 to 2021, more than 80\%
of the data during daytime can be removed. The remaining 20\% samples have
equal prediction power for training models. This result suggests that indeed
large traffic datasets can be subdivided into significantly smaller but equally
informative datasets. From these findings, we conclude that the proposed
methodology proves valuable in evaluating large traffic datasets' true
information content. Further extensions, such as extracting smaller, spatially
non-redundant datasets, are possible with this method.Comment: 13 pages, 5 figure
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Multivariate time series forecasting is an important machine learning problem
across many domains, including predictions of solar plant energy output,
electricity consumption, and traffic jam situation. Temporal data arise in
these real-world applications often involves a mixture of long-term and
short-term patterns, for which traditional approaches such as Autoregressive
models and Gaussian Process may fail. In this paper, we proposed a novel deep
learning framework, namely Long- and Short-term Time-series network (LSTNet),
to address this open challenge. LSTNet uses the Convolution Neural Network
(CNN) and the Recurrent Neural Network (RNN) to extract short-term local
dependency patterns among variables and to discover long-term patterns for time
series trends. Furthermore, we leverage traditional autoregressive model to
tackle the scale insensitive problem of the neural network model. In our
evaluation on real-world data with complex mixtures of repetitive patterns,
LSTNet achieved significant performance improvements over that of several
state-of-the-art baseline methods. All the data and experiment codes are
available online.Comment: Accepted by SIGIR 201
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