439 research outputs found
A review of probabilistic forecasting and prediction with machine learning
Predictions and forecasts of machine learning models should take the form of
probability distributions, aiming to increase the quantity of information
communicated to end users. Although applications of probabilistic prediction
and forecasting with machine learning models in academia and industry are
becoming more frequent, related concepts and methods have not been formalized
and structured under a holistic view of the entire field. Here, we review the
topic of predictive uncertainty estimation with machine learning algorithms, as
well as the related metrics (consistent scoring functions and proper scoring
rules) for assessing probabilistic predictions. The review covers a time period
spanning from the introduction of early statistical (linear regression and time
series models, based on Bayesian statistics or quantile regression) to recent
machine learning algorithms (including generalized additive models for
location, scale and shape, random forests, boosting and deep learning
algorithms) that are more flexible by nature. The review of the progress in the
field, expedites our understanding on how to develop new algorithms tailored to
users' needs, since the latest advancements are based on some fundamental
concepts applied to more complex algorithms. We conclude by classifying the
material and discussing challenges that are becoming a hot topic of research.Comment: 83 pages, 5 figure
Modelling systemic risk using neural network quantile regression
We propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. Building on the estimation results, we model systemic risk spillover effects in a network context across banks by considering the marginal effects of the quantile regression procedure. An out-of-sample analysis shows great performance compared to a linear baseline specification, signifying the importance that nonlinearity plays for modelling systemic risk. We then propose three network-based measures from our fitted results. First, we use the Systemic Network Risk Index (SNRI) as a measure for total systemic risk. A comparison to the existing network-based risk measures reveals that our approach offers a new perspective on systemic risk due to the focus on the lower tail and to the allowance for nonlinear effects. We also introduce the Systemic Fragility Index (SFI) and the Systemic Hazard Index (SHI) as firm-specific measures, which allow us to identify systemically relevant firms during the financial crisis.Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Peer Reviewe
Effects of Transit Signal Priority on Traffic Safety: Interrupted Time Series Analysis of Portland, Oregon, Implementations
Transit signal priority (TSP) has been implemented to transit systems in many
cities of the United States. In evaluating TSP systems, more attention has been
given to its operational effects than to its safety effects. Existing studies
assessing safety effects of TSP reported mixed results, indicating that the
safety effects of TSP vary in different contexts. In this study, TSP
implementations in Portland, Oregon, were assessed using interrupted time
series analysis (ITSA) on month-to-month changes in number of crashes from
January 1995 to December 2010. Single-group and controlled ITSA were conducted
for all crashes, property-damage-only crashes, fatal and injury crashes,
pedestrian-involved crashes, and bike-involved crashes. Evaluation of the
post-intervention period (2003 to 2010) showed a reduction in all crashes on
street sections with TSP (-4.5 percent), comparing with the counterfactual
estimations based on the control group data. The reduction in
property-damage-only crashes (-10.0 percent) contributed the most to the
overall reduction. Fatal and injury crashes leveled out after TSP
implementation but did not change significantly comparing with the control
group. Pedestrian and bike-involved crashes were found to increase in the
post-intervention period with TSP, comparing with the control group. Potential
reasons to these TSP effects on traffic safety were discussed.Comment: Published in Accident Analysis & Preventio
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