1,606 research outputs found
Forecasting monthly airline passenger numbers with small datasets using feature engineering and a modified principal component analysis
In this study, a machine learning approach based on time series models, different feature engineering, feature extraction, and feature derivation is proposed to improve air passenger forecasting. Different types of datasets were created to extract new features from the core data. An experiment was undertaken with artificial neural networks to test the performance of neurons in the hidden layer, to optimise the dimensions of all layers and to obtain an optimal choice of connection weights – thus the nonlinear optimisation problem could be solved directly. A method of tuning deep learning models using H2O (which is a feature-rich, open source machine learning platform known for its R and Spark integration and its ease of use) is also proposed, where the trained network model is built from samples of selected features from the dataset in order to ensure diversity of the samples and to improve training. A successful application of deep learning requires setting numerous parameters in order to achieve greater model accuracy. The number of hidden layers and the number of neurons, are key parameters in each layer of such a network. Hyper-parameter, grid search, and random hyper-parameter approaches aid in setting these important parameters. Moreover, a new ensemble strategy is suggested that shows potential to optimise parameter settings and hence save more computational resources throughout the tuning process of the models. The main objective, besides improving the performance metric, is to obtain a distribution on some hold-out datasets that resemble the original distribution of the training data. Particular attention is focused on creating a modified version of Principal Component Analysis (PCA) using a different correlation matrix – obtained by a different correlation coefficient based on kinetic energy to derive new features. The data were collected from several airline datasets to build a deep prediction model for forecasting airline passenger numbers. Preliminary experiments show that fine-tuning provides an efficient approach for tuning the ultimate number of hidden layers and the number of neurons in each layer when compared with the grid search method. Similarly, the results show that the modified version of PCA is more effective in data dimension reduction, classes reparability, and classification accuracy than using traditional PCA.</div
Memory-Gated Recurrent Networks
The essence of multivariate sequential learning is all about how to extract
dependencies in data. These data sets, such as hourly medical records in
intensive care units and multi-frequency phonetic time series, often time
exhibit not only strong serial dependencies in the individual components (the
"marginal" memory) but also non-negligible memories in the cross-sectional
dependencies (the "joint" memory). Because of the multivariate complexity in
the evolution of the joint distribution that underlies the data generating
process, we take a data-driven approach and construct a novel recurrent network
architecture, termed Memory-Gated Recurrent Networks (mGRN), with gates
explicitly regulating two distinct types of memories: the marginal memory and
the joint memory. Through a combination of comprehensive simulation studies and
empirical experiments on a range of public datasets, we show that our proposed
mGRN architecture consistently outperforms state-of-the-art architectures
targeting multivariate time series.Comment: This paper was accepted and will be published in the Thirty-Fifth
AAAI Conference on Artificial Intelligence (AAAI-21
Learning to Simulate: Generative Metamodeling via Quantile Regression
Stochastic simulation models, while effective in capturing the dynamics of
complex systems, are often too slow to run for real-time decision-making.
Metamodeling techniques are widely used to learn the relationship between a
summary statistic of the outputs (e.g., the mean or quantile) and the inputs of
the simulator, so that it can be used in real time. However, this methodology
requires the knowledge of an appropriate summary statistic in advance, making
it inflexible for many practical situations. In this paper, we propose a new
metamodeling concept, called generative metamodeling, which aims to construct a
"fast simulator of the simulator". This technique can generate random outputs
substantially faster than the original simulation model, while retaining an
approximately equal conditional distribution given the same inputs. Once
constructed, a generative metamodel can instantaneously generate a large amount
of random outputs as soon as the inputs are specified, thereby facilitating the
immediate computation of any summary statistic for real-time decision-making.
Furthermore, we propose a new algorithm -- quantile-regression-based generative
metamodeling (QRGMM) -- and study its convergence and rate of convergence.
Extensive numerical experiments are conducted to investigate the empirical
performance of QRGMM, compare it with other state-of-the-art generative
algorithms, and demonstrate its usefulness in practical real-time
decision-making.Comment: Main body: 36 pages, 7 figures; supplemental material: 12 page
Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events
Optimal traffic control under incident-driven congestion is crucial for road safety and maintaining network performance. Over the last decade, prediction and simulation of road traffic play important roles in network operation. This dissertation focuses on development of a machine learning-based prediction model, a stochastic cell transmission model (CTM), and an optimisation model. Numerical studies were performed to evaluate the proposed models. The results indicate that proposed models are helpful for road management during road incidents
Towards Autonomous Computer Networks in Support of Critical Systems
L'abstract è presente nell'allegato / the abstract is in the attachmen
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Time series are the primary data type used to record dynamic system
measurements and generated in great volume by both physical sensors and online
processes (virtual sensors). Time series analytics is therefore crucial to
unlocking the wealth of information implicit in available data. With the recent
advancements in graph neural networks (GNNs), there has been a surge in
GNN-based approaches for time series analysis. Approaches can explicitly model
inter-temporal and inter-variable relationships, which traditional and other
deep neural network-based methods struggle to do. In this survey, we provide a
comprehensive review of graph neural networks for time series analysis
(GNN4TS), encompassing four fundamental dimensions: Forecasting,
classification, anomaly detection, and imputation. Our aim is to guide
designers and practitioners to understand, build applications, and advance
research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy
of GNN4TS. Then, we present and discuss representative research works and,
finally, discuss mainstream applications of GNN4TS. A comprehensive discussion
of potential future research directions completes the survey. This survey, for
the first time, brings together a vast array of knowledge on GNN-based time
series research, highlighting both the foundations, practical applications, and
opportunities of graph neural networks for time series analysis.Comment: 27 pages, 6 figures, 5 table
- …