Jeroen V.K. Rombouts
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View article: Leveraging Open-Source Tools to Analyse Ground-Based Forest LiDAR Data in South Australian Forests
Leveraging Open-Source Tools to Analyse Ground-Based Forest LiDAR Data in South Australian Forests Open
This paper investigates the application of open-source software and methods for forest LiDAR analysis, with a focus on enhancing forest inventory metrics in the radiata pine forests of South Australia’s Green Triangle region. A semi-system…
View article: Cross-temporal forecast reconciliation at digital platforms with machine learning
Cross-temporal forecast reconciliation at digital platforms with machine learning Open
Platform businesses operate on a digital core, and their decision-making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to day…
View article: Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms
Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms Open
Practice- and policy-oriented abstract: The success of on-demand service platforms crucially hinges upon their ability to make fast and accurate demand forecasts so that its workers are always at the right time and location to serve custom…
View article: Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning
Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning Open
Platform businesses operate on a digital core and their decision making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days…
View article: Monitoring Machine Learning Forecasts for Platform Data Streams
Monitoring Machine Learning Forecasts for Platform Data Streams Open
Data stream forecasts are essential inputs for decision making at digital platforms. Machine learning algorithms are appealing candidates to produce such forecasts. Yet, digital platforms require a large-scale forecast framework that can f…
View article: Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms
Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms Open
On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable, a…
View article: Dynamics of Variance Risk Premia, Investors' Sentiment and Return Predictability
Dynamics of Variance Risk Premia, Investors' Sentiment and Return Predictability Open
We develop a joint framework linking the physical variance and its risk neutral expectation implying variance risk premia that are persistent, appropriately reacting to changes in level and variability of the variance and naturally satisfy…
View article: Lasso-Based Forecast Combinations for Forecasting Realized Variances
Lasso-Based Forecast Combinations for Forecasting Realized Variances Open
Volatility forecasts are key inputs in financial analysis. While lasso based forecasts have shown to perform well in many applications, their use to obtain volatility forecasts has not yet received much attention in the literature. Lasso e…