Prediction with expert advice for the Brier game

Vovk, Vladimir; Zhdanov, Fedor

doi:10.1145/1390156.1390295

Cited by 20 publications

(33 citation statements)

References 22 publications

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“…We note that the use of side information in the framework of prediction with expert advice was previously addressed, e.g., in [4,11,14]. However, these approaches differ from ours, as they assume white box models, i.e., that they know how the experts make their predictions, and use a state-independent weighting.…”

Section: B State-dependent Aggregationmentioning

confidence: 98%

“…Constraint (13) ensures that the energy balance in the system is maintained. Equality (14) defines the state of charge in the battery based on the charge and discharge rates. Finally, box constraints (15-18) define the range of the variables.…”

Section: Controlling the Energy Flowmentioning

confidence: 99%

“…Previously, the framework of prediction with expert advice was used to predict energy consumption in [7], and online learning with side information was investigated, for example, in [4,11,14]. As we will later see in Section V-B, our approach differs from the ones above and one of our main contributions is to use situation dependent losses during prediction which allows the system to adapt to the changing circumstances.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive aggregated predictions for renewable energy systems

Csáji

Kovács

Váncza

2014

2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

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Abstract-The paper addresses the problem of generating forecasts for energy production and consumption processes in a renewable energy system. The forecasts are made for a prototype public lighting microgrid, which includes photovoltaic panels and LED luminaries that regulate their lighting levels, as inputs for a receding horizon controller. Several stochastic models are fitted to historical times-series data and it is argued that side information, such as clear-sky predictions or typical system behaviors, can be used as exogenous inputs to increase their performance. The predictions can be further improved by combining the forecasts of several models using online learning, the framework of prediction with expert advice. The paper suggests an adaptive aggregation method which also takes side information into account, and makes a state-dependent aggregation. Numerical experiments are presented, as well, showing the efficiency of the estimated timeseries models and the proposed aggregation approach.

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Section: B State-dependent Aggregationmentioning

confidence: 98%

Section: Controlling the Energy Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive aggregated predictions for renewable energy systems

Csáji

Kovács

Váncza

2014

2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

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“…The literature has confronted some of these challenges but many remain unresolved. 23,24,25,26,27,28 Second, in order for our results to accurately reflect what historical performance would have been, we must ensure that models produce only out-of-sample predictions -that is, models must not incorporate any insample or "future" information that would not have been knowable as of the simulated date. For example, we cannot use information about a participant's accuracy or rank in February of 2015 to predict a case from January of 2015.…”

Section: A Research Principlesmentioning

confidence: 99%

Crowdsourcing Accurately and Robustly Predicts Supreme Court Decisions

2017

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Scholars have increasingly investigated "crowdsourcing" as an alternative to expert-based judgment or purely data-driven approaches to predicting the future. Under certain conditions, scholars have found that crowdsourcing can outperform these other approaches. However, despite interest in the topic and a series of successful use cases, relatively few studies have applied empirical model thinking to evaluate the accuracy and robustness of crowdsourcing in real-world contexts. In this paper, we offer three novel contributions. First, we explore a dataset of over 600,000 predictions from over 7,000 participants in a multi-year tournament to predict the decisions of the Supreme Court of the United States. Second, we develop a comprehensive crowd construction framework that allows for the formal description and application of crowdsourcing to real-world data. Third, we apply this framework to our data to construct more than 275,000 crowd models. We find that in out-of-sample historical simulations, crowdsourcing robustly outperforms the commonly-accepted null model, yielding the highest-known performance for this context at 80.8% case level accuracy. To our knowledge, this dataset and analysis represent one of the largest explorations of recurring human prediction to date, and our results provide additional empirical support for the use of crowdsourcing as a prediction method.

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“…In [5] the methods of prediction with expert advice are applied to aggregate predictions of sports results calculated from the odds quoted by bookmakers. The numbers of bookmakers were very small, four in one empirical study and eight in another.…”

Section: Introductionmentioning

confidence: 99%

Specialist Experts for Prediction with Side Information

Kalnishkan

Adamskiy

Chernov

et al. 2015

2015 IEEE International Conference on Data Mining Workshop (ICDMW)

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Abstract-The paper proposes the vicinities merging algorithm for prediction with side information. The algorithm is based on specialist experts techniques. We use vicinities in the side information domain to identify relevant past examples, apply standard learning techniques to them, and then use prediction with expert advice tools to merge those predictions. Guarantees from the theory of prediction with expert advice ensure that helpful vicinities are selected dynamically. The algorithm automatically converges on the right vicinities from an initial broad selection. We apply the resulting algorithms to two problems, prediction of implied volatility of options and prediction of students' performance at tests. On the problem of predicting implied volatility, the algorithm consistently outperforms naive competitors and a highly-tuned proprietary method used in the industry. When applied to the students' performance, the algorithm never falls behind the baseline and outperforms it when the side information is beneficial.

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Prediction with expert advice for the Brier game

Cited by 20 publications

References 22 publications

Adaptive aggregated predictions for renewable energy systems

Adaptive aggregated predictions for renewable energy systems

Crowdsourcing Accurately and Robustly Predicts Supreme Court Decisions

Specialist Experts for Prediction with Side Information

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