Short-term demand forecasting for real-time operational control of the Barcelona water transport network

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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Real-Time Automated Detection and Quantitative Analysis of Seizures and Short-Term Prediction of Clinical Onset

Epilepsia ◽

10.1111/j.1528-1157.1998.tb01430.x ◽

1998 ◽

Vol 39 (6) ◽

pp. 615-627 ◽

Cited By ~ 249

Author(s):

Ivan Osorio ◽

Mark G. Frei ◽

Steven B. Wilkinson

Keyword(s):

Quantitative Analysis ◽

Real Time ◽

Automated Detection ◽

Short Term ◽

Clinical Onset ◽

Term Prediction ◽

Short Term Prediction

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Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽

10.3390/en14113204 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3204

Author(s):

Michał Sabat ◽

Dariusz Baczyński

Keyword(s):

Energy Demand ◽

Demand Forecasting ◽

Electric Energy ◽

Forecasting Model ◽

Short Term ◽

Complex Evaluation ◽

Proposed Model ◽

Energy Demand Forecasting ◽

Pareto Fronts ◽

Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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Nowcasting global trade in goods and services

Statistical Journal of the IAOS ◽

10.3233/sji-200716 ◽

2020 ◽

pp. 1-19

Author(s):

Fernando Cantú-Bazaldúa

Keyword(s):

Real Time ◽

Relevant Information ◽

Global Trade ◽

Dynamic Factor ◽

Timely Manner ◽

Short Term ◽

Dynamic Factor Models ◽

Significant Information ◽

Goods And Services ◽

World Economic

World economic aggregates are compiled infrequently and released after considerable lags. There are, however, many potentially relevant series released in a timely manner and at a higher frequency that could provide significant information about the evolution of global aggregates. The challenge is then to extract the relevant information from this multitude of indicators and combine it to track the real-time evolution of the target variables. We develop a methodology based on dynamic factor models adapted for variables with heterogeneous frequencies, ragged ends and missing data. We apply this methodology to nowcast global trade in goods in goods and services. In addition to monitoring these variables in real time, this method can also be used to obtain short-term forecasts based on the most up-to-date values of the underlying indicators.

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