We consider a general class of forecasting protocols, called "linear protocols", and discuss several important special cases, including multi-class forecasting. Forecasting is formalized as a game between three players: Reality, whose role is to generate observations; Forecaster, whose goal is to predict the observations; and Skeptic, who tries to make money on any lack of agreement between Forecaster's predictions and the actual observations. Our main mathematical result is that for any continuous strategy for Skeptic in a linear protocol there exists a strategy for Forecaster that does not allow Skeptic's capital to grow. This result is a meta-theorem that allows one to transform any continuous law of probability in a linear protocol into a forecasting strategy whose predictions are guaranteed to satisfy this law. We apply this meta-theorem to a weak law of large numbers in Hilbert spaces to obtain a version of the K29 prediction algorithm for linear protocols and show that this version also satisfies the attractive properties of proper calibration and resolution under a suitable choice of its kernel parameter, with no assumptions about the way the data is generated.
The method of defensive forecasting is applied to the problem of prediction with expert advice for binary outcomes. It turns out that defensive forecasting is not only competitive with the Aggregating Algorithm but also handles the case of "second-guessing" experts, whose advice depends on the learner's prediction; this paper assumes that the dependence on the learner's prediction is continuous.
Defensive forecasting is a method of transforming laws of probability (stated in game-theoretic terms as strategies for Sceptic) into forecasting algorithms. There are two known varieties of defensive forecasting: "continuous", in which Sceptic's moves are assumed to depend on the forecasts in a (semi)continuous manner and which produces deterministic forecasts, and "randomized", in which the dependence of Sceptic's moves on the forecasts is arbitrary and Forecaster's moves are allowed to be randomized. This note shows that the randomized variety can be obtained from the continuous variety by …
Departments of Computer Science & Engineering and Data Science, New York University（纽约大学计算机科学与工程系和数据科学系）；Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系）
We consider how to make probability forecasts of binary labels. Our main mathematical result is that for any continuous gambling strategy used for detecting disagreement between the forecasts and the actual labels, there exists a forecasting strategy whose forecasts are ideal as far as this gambling strategy is concerned. A forecasting strategy obtained in this way from a gambling strategy demonstrating a strong law of large numbers is simplified and studied empirically.
Computer Learning Research Centre（计算机学习研究中心）；Royal Holloway, University of London（伦敦大学皇家霍洛威学院）
We apply the method of defensive forecasting, based on the use of game-theoretic supermartingales, to prediction with expert advice. In the traditional setting of a countable number of experts and a finite number of outcomes, the Defensive Forecasting Algorithm is very close to the well-known Aggregating Algorithm. Not only the performance guarantees but also the predictions are the same for these two methods of fundamentally different nature. We discuss also a new setting where the experts can give advice conditional on the learner's future decision. Both the algorithms can be adapted to the …
In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts. In contrast to the NormalHedge bound, which mainly depends on the effective number of experts and also weakly depends on the nominal one, we obtain a bound that does not contain the nominal number of experts at all. We use the defensive forecasting method and introduce an application of defensive forecasting to multivalued supermartingales.
Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting
Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Sweden（诊断与介入部门，辐射物理，生物医学工程，乌梅大学，瑞典）；Department of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, Italy（海军、电气、电子与电信工程部门，热那亚大学，意大利）
TimeSynth: A Framework for Uncovering Systematic Biases in Time Series Forecasting
Scientific Computing and Imaging Institute, University of Utah（科学计算与成像研究所，犹他大学）；Kahlert School of Computing, University of Utah（Kahlert计算学院，犹他大学）；Mountain Biometrics Inc.（山景生物医学公司）；Department of Psychiatry, University of Utah（精神病学系，犹他大学）
Rolling-Origin Validation Reverses Model Rankings in Multi-Step PM10 Forecasting: XGBoost, SARIMA, and Persistence
Departamento de Ingeniería de Computadores, Universidad Miguel Hernández（计算机工程系，米格尔·赫尔内斯大学）；Departamento de Física Aplicada, Universidad Miguel Hernández（应用物理系，米格尔·赫尔内斯大学）
Foundation for Research and Technology-Hellas（希腊基础研究与技术基金会）；Technical University of Crete（克里特理工大学）
Modern Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices, with the data acquisition and processing infrastructure setting restrictions in terms of computational power and energy resources. To alleviate this issue, sensors are often configured to operate at relatively low sampling frequencies, yielding a reduced set of observations. Nevertheless, this can hamper dramatically subsequent decision-making, such as forecasting. To address this problem, in this work we evaluate short-term forecasting in highly underdetermined cases, i.e., the number…
We introduce a new protocol for prediction with expert advice in which each expert evaluates the learner's and his own performance using a loss function that may change over time and may be different from the loss functions used by the other experts. The learner's goal is to perform better or not much worse than each expert, as evaluated by that expert, for all experts simultaneously. If the loss functions used by the experts are all proper scoring rules and all mixable, we show that the defensive forecasting algorithm enjoys the same performance guarantee as that attainable by the Aggregating…
Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition
KP Labs（KP实验室）；Silesian University of Technology（西里西亚技术大学）；Warsaw University of Technology, Centre of Credible AI（华沙技术大学可信人工智能中心）；Indian Institute of Technology Patna（印度理工学院帕纳瓦分校）；European Space Agency, European Space Operations Center（欧洲航天局欧洲空间操作中心）
Comparative Analysis of Modern Machine Learning Models for Retail Sales Forecasting
Faculty of Electrical Engineering and Computing, University of Zagreb（Zagreb大学电气工程与计算学院）；It From Bit d.o.o.（It From Bit公司）；Babson College（巴布森学院）；mStart Plus d.o.o.（mStart Plus公司）
Asymmetric Peak-Aware Loss for Peak-Critical Time Series Forecasting
Department of Transport and Planning, Faculty of Civil Engineering and Geosciences（交通与规划系，土木与地球科学学院）；Mobility Innovation Centre Delft University of Technology（代尔夫特理工大学移动创新中心）；Delft University of Technology（代尔夫特理工大学）
In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts. In contrast to the Normal- Hedge bound, which mainly depends on the effective number of experts but also weakly depends on the nominal one, we obtain a bound that does not contain the nominal number of experts at all. We use the defensive forecasting method and introduce an application of defensive forecasting to multivalued supermartingales.
Universidad Nacional de Educación a Distancia — UNED（西班牙国立远程教育大学）；Inverence
Air quality has become one of the main issues in public health and urban planning management, due to the proven adverse effects of high pollutant concentrations. Considering the mitigation measures that cities all over the world are taking in order to face frequent low air quality episodes, the capability of foreseeing future pollutant concentrations is of great importance. Through this paper, we present SOCAIRE, an operational tool based on a Bayesian and spatiotemporal ensemble of neural and statistical nested models. SOCAIRE integrates endogenous and exogenous information in order to predic…
Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting
School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）；China Electric Power Research Institute（中国电力科学研究院）
