Forecasting House Prices through Credit Conditions: A Bayesian Approach

As housing development and housing market policies involve many long-term decisions, improving house price predictions could benefit the functioning of the housing market. Therefore, in this paper, we investigate how house price predictions can be improved. In particular, the merits of Bayesian estimation techniques in enhancing house price...

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no...

Near-real-time satellite precipitation data ingestion into peak runoff forecasting models

Extreme peak runoff forecasting is still a challenge in hydrology. In fact, the use of traditional physically-based models is limited by the lack of sufficient data and the complexity of the inner hydrological processes. Here, we employ a Machine Learning technique, the Random Forest (RF) together with a combination of Feature Engineering (FE...

From speculation to reality: Enhancing anticipatory ethics for emerging technologies (ATE) in practice

Various approaches have emerged over the last several decades to meet the challenges and complexities of anticipating and responding to the potential impacts of emerging technologies. Although many of the existing approaches share similarities, they each have shortfalls. This paper takes as the object of its study Anticipatory Ethics for...

Individual yield nowcasting for residential PV systems

Due to the inherent uncertainty in photovoltaic (PV) energy generation, an accurate power forecasting is essential to ensure a reliable operation of PV systems and a safe electric grid. Machine learning (ML) techniques have gained popularity on the development of this task due to its increased accuracy. Most literature, however, focuses only...

Forecasting Graph Signals with Recursive MIMO Graph Filters

Forecasting time series on graphs is a fundamental problem in graph signal processing. When each entity of the network carries a vector of values for each time stamp instead of a scalar one, existing approaches resort to the use of product graphs to combine this multidimensional information, at the expense of creating a larger graph. In this...

Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow

Short-term forecasting of passenger flow is critical for transit management and crowd regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven by other latent factors, and exogenous factors bring challenges to the short-term forecasts of passenger flow of urban rail transit networks. An innovative deep...

Stationary vine copula models for multivariate time series

Multivariate time series exhibit two types of dependence: across variables and across time points. Vine copulas are graphical models for the dependence and can conveniently capture both types of dependence in the same model. We derive the maximal class of graph structures that guarantee stationarity under a natural and verifiable condition...

Monitoring and forecasting of seismic wavefields in the subsurface

Monitoring seismic wavefields caused by induced seismicity in the subsurface is a difficult process. Ideally, it requires physical receivers in the subsurface, which is unpractical. Frequently, only measurements at the surface of the Earth are available, which give a limited amount of information about the subsurface. One way to improve the...

Decision-Making in Early Internationalization: A Structured Expert Judgement Approach

The aim of this chapter is to show how a structured approach to elicit expert judgement (SEJ) can guide the practice of early internationalization. We applied SEJ to forecast some critical issues upon which an innovative start-up wished to base their decision of whether to expand their initial operations in Poland and Czech Republic to Brazil...

Forecasting Multi-Dimensional Processes Over Graphs

The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a vector of quantities rather than a scalar one. To tackle this issue, we devise a new framework and...

Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting

Participation of wind energy in the generation portfolio of power systems is increasing, making it more challenging for system operators to adequately maintain system security. It therefore becomes increasingly crucial to accurately predict the wind generation. This work investigates how different parameters influence the performance of...

Forecasting pedestrian movements using recurrent neural networks: An application of crowd monitoring data

Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd flows a longer time period ahead. Moreover, most...

When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents

When fully automated cars will be widespread is a question that has attracted considerable attention from futurists, car manufacturers, and academics. This paper aims to poll the public's expectations regarding the deployment of fully automated cars. In 15 crowdsourcing surveys conducted between June 2014 and January 2019, we obtained answers...

Exploring the role of system operation modes in failure analysis in the context of first generation cyber-physical systems

Typically, emerging system failures have a strong impact on the performance of industrial systems as well as on the efficiency of their operational and servicing processes. Being aware of these, maintenance and repair researchers have developed multiple failure detection and diagnosis techniques that allow early recognition of system or...

Improving the link between the futures field and policymaking

Policymakers need to make policies for unknown and uncertain futures. Researchers in the futures field have a great deal to contribute to the policymaking process. But, futures research is often neglected as an element of policymaking. The aim of this paper is to improve the link between futures research and policymaking. More specifically,...

A generalized model for short-term forecasting of solar irradiance

In recent years, as the share of solar power in the electrical grid has been increasing, accurate methods for forecasting solar irradiance have become necessary to manage the electrical grid. More specifically, as solar generators are geographically dispersed, it is very important to have general models that can predict solar irradiance without...

Parametric Evaluation of Different ANN Architectures: Forecasting Wind Power Across Different Time Horizons

The participation of volatile wind energy resources in the generation mix of power systems is increasing. It is therefore becoming more and more crucial for system operators to accurately predict the wind power generation across different short term horizons (5 to 60 minutes ahead) in order to adequately balance the system and maintain system...