arXiv research

New method predicts wave height exceedance probabilities.

The study examines the discrepancies between binary forecasts and real-world outcomes, revealing their often misleading nature.

Develops forecast hedging for improved calibration of forecasts.

We present a simple approach to forecasting conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regression that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributi…

Parity calibration aims to predict increase-decrease events, not values.

Machine learning struggles to predict binary options movements due to randomness.

BinConv improves time series forecasting by preserving ordinal information in a classification framework.

Improved upper bound for online calibrated forecasting of binary sequences.

Develops a new volatility model for prediction markets.

Develops a new volatility model for prediction markets.

Critiques binary classification evaluation methods, advocating for proper scoring rules.

Paper explores learning patterns in binary sequences, finding no method consistently outperforms others.

Paper proves a new lower bound on calibration error for binary prediction.

New method achieves faster calibration without randomization.

Foresight Arena benchmarks AI forecasting on real-world markets, isolating predictive edge.

New algorithms reduce label collection for online prediction with expert advice.

We study the multiclass online learning problem where a forecaster makes a sequence of predictions using the advice of $n$ experts. Our main contribution is to analyze the regime where the best expert makes at most $b$ mistakes and to show that when $b = o(\log_4{n})$, the expected number of mistakes made by the optima…

We consider the forecast aggregation problem in repeated settings, where the forecasts are done on a binary event. At each period multiple experts provide forecasts about an event. The goal of the aggregator is to aggregate those forecasts into a subjective accurate forecast. We assume that experts are Bayesian; namely…

A new calibration metric bridges testability and actionability.

Proposes a model for time-varying regression coefficients.

A new method uses deep learning to predict full conditional distributions.

In the practice of point prediction, it is desirable that forecasters receive a directive in the form of a statistical functional, such as the mean or a quantile of the predictive distribution. When evaluating and comparing competing forecasts, it is then critical that the scoring function used for these purposes be co…

Deep learning predicts lightning strikes with high accuracy.

Proposes and evaluates three diagnostic graphics for probabilistic classifiers.

Study compares two methods for predicting extreme atmospheric events.

We consider the estimation of binary election outcomes as martingales and propose an arbitrage pricing when one continuously updates estimates. We argue that the estimator needs to be priced as a binary option as the arbitrage valuation minimizes the conventionally used Brier score for tracking the accuracy of probabil…

The paper uses machine learning to detect malicious executable files.

The paper studies the distance from calibration in sequential prediction, proving upper and lower bounds.

We provide yet another proof of the existence of calibrated forecasters; it has two merits. First, it is valid for an arbitrary finite number of outcomes. Second, it is short and simple and it follows from a direct application of Blackwell's approachability theorem to carefully chosen vector-valued payoff function and …

This master's thesis discusses an important issue regarding how algorithmic decision making (ADM) is used in crime forecasting. In America forecasting tools are widely used by judiciary systems for making decisions about risk offenders based on criminal justice for risk offenders. By making use of such tools, the judic…

Cubic predicts stock market indices by fusing stock latent embeddings and converting to binary classification.

Improves sampling speed of ARM models without losing accuracy.

LSTM neural networks improve stock price prediction for Stockholm OMX30.

We analyze the dynamics of a forecasting game which exhibits the phenomenon of information cascades. Each agent aims at correctly predicting a binary variable and he/she can either look for independent information or herd on the choice of others. We show that dynamics can be analitically described in terms of a Langevi…

A key problem in financial mathematics is the forecasting of financial crashes: if we perturb asset prices, will financial institutions fail on a massive scale? This was recently shown to be a computationally intractable (NP-hard) problem. Financial crashes are inherently difficult to predict, even for a regulator whic…

We address online linear optimization problems when the possible actions of the decision maker are represented by binary vectors. The regret of the decision maker is the difference between her realized loss and the best loss she would have achieved by picking, in hindsight, the best possible action. Our goal is to unde…

This paper uses MIL and MHCNN-RNN to predict precursors to aviation safety events.

LLMs outperform strong tabular baselines on industrial car retrofit prediction.

Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary…

EDINET-Bench evaluates LLMs on complex financial tasks using Japanese financial statements.

Transformer pre-training improves stock return prediction accuracy.

A Bayesian framework models dynamic probability predictions over time.

This paper concerns sequential computation of risk measures for financial data and asks how, given a risk measurement procedure, we can tell whether the answers it produces are `correct'. We draw the distinction between `external' and `internal' risk measures and concentrate on the latter, where we observe data in real…

We address the online linear optimization problem when the actions of the forecaster are represented by binary vectors. Our goal is to understand the magnitude of the minimax regret for the worst possible set of actions. We study the problem under three different assumptions for the feedback: full information, and the …

Simpler models outperform deep architectures with proper preprocessing and tuning.

New method detects TC imagery patterns for rapid intensity change.

Optimal reconciliation keeps some forecasts unchanged in hierarchical forecasting.

Paper proposes a framework for probabilistic load forecasting by integrating point forecasts.