In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Highway Capacity Manual改訂の動向--テイラ-教授の講演より

527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Learning from class-imbalanced data

Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCN .

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling. We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines.

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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

The shocks of armed conflicts to renewable energy finance: Empirical evidence from cross-country data

Road accidents usually cause great congestions, and shock waves generate due to the congestions, which severely affect travel time reliability (TTR) of upstream vehicles. Connected vehicles can get and spread information of the accident vehicles, which assists drivers in estimating TTR of the road and making better route choice. The major work of this paper is the proposal of a TTR model to calculate the TTR of freeway affected by accident. Information of time delay, accident position, accident duration and traffic flow status, which can be obtained from connected vehicles, are used. To better explain the TTR model, a simulation case analysis is given, effect of accident duration variance on TTR is also tested. Results show that TTR model in freeway affected by accident based on connected vehicles is practical, and the TTR increases with the variance. The research results are helpful to drivers.

Travel Time Reliability Affected by Accident in Freeway with Connected Vehicles

ABSTRACT This research constructs a double difference model to test the impact of LCCP on green innovation by using urban panel data of 276 cities in China from 2005 to 2019. Findings show that LCCP effectively improves the level of green innovation performance, and the conclusion is robust. Its effect is more obvious in cities with high scientific and educational levels as well as big cities or eastern cities of the country. LCCP enhances the performance of urban green innovation by driving technological innovation, upgrading the industrial structure, and optimizing urban public services. This research is significant for realizing high-quality economic development.

Impact of Low-Carbon City Construction Policy on Green Innovation Performance in China

In this study, a lD Brownian movement formula that considers the uncertainty of initial parameters was derived and a time-dependent model of load and structural parameters established. A model for reliability and its corresponding sensitivity analysis was proposed as well, according to the residual strength distribution calculated based on the S−N characteristics of the material and stress-strength interference theory. This model considers the effects of interaction under uncertain structural parameters and strength as well as the uncertainty of actual use. On this basis, the variation rules of initial parameters, strength, load, reliability, and its sensitivity with time are analyzed; those of reliability, failure rate, and reliability sensitivity with time were calculated when the driving axle of a vehicle was taken as an example.

Yunpeng Wang

Papers

The shocks of armed conflicts to renewable energy finance: Empirical evidence from cross-country data

Travel Time Reliability Affected by Accident in Freeway with Connected Vehicles

Impact of Low-Carbon City Construction Policy on Green Innovation Performance in China

Analysis of Time-Dependent Reliability and Sensitivity of Vehicle Components

An identification model of critical control sub-regions based on macroscopic fundamental diagram theory