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Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of Analystical Methodology
In the last two decades, a number of methods have been proposed for fore...
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Dynamic Network Models for Forecasting
We have developed a probabilistic forecasting methodology through a synt...
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Forecasting Future Sequence of Actions to Complete an Activity
Future human action forecasting from partial observations of activities ...
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Machine Learning for Spatiotemporal Sequence Forecasting: A Survey
Spatiotemporal systems are common in the real-world. Forecasting the mul...
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Deep Factors with Gaussian Processes for Forecasting
A large collection of time series poses significant challenges for class...
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How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting
In a variety of business situations, the introduction or improvement of ...
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An Interesting Uncertainty-Based Combinatoric Problem in Spare Parts Forecasting: The FRED System
The domain of spare parts forecasting is examined, and is found to prese...
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Rank Position Forecasting in Car Racing
Forecasting is challenging since uncertainty resulted from exogenous factors exists. This work investigates the rank position forecasting problem in car racing, which predicts the rank positions at the future laps for cars. Among the many factors that bring changes to the rank positions, pit stops are critical but irregular and rare. We found existing methods, including statistical models, machine learning regression models, and state-of-the-art deep forecasting model based on encoder-decoder architecture, all have limitations in the forecasting. By elaborative analysis of pit stops events, we propose a deep model, RankNet, with the cause effects decomposition that modeling the rank position sequence and pit stop events separately. It also incorporates probabilistic forecasting to model the uncertainty inside each sub-model. Through extensive experiments, RankNet demonstrates a strong performance improvement over the baselines, e.g., MAE improves more than 10 consistently, and is also more stable when adapting to unseen new data. Details of model optimization, performance profiling are presented. It is promising to provide useful forecasting tools for the car racing analysis and shine a light on solutions to similar challenging issues in general forecasting problems.
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