The paper proposes K-line--Quantile Sequential Projection (KQSP), a method to reconcile quantile and K-line crossing inconsistencies in probabilistic K-line forecasting without reordering, specialized…

This paper introduces Susceptible Architectures (SUSA) for volatility forecasting using complex-valued reservoirs and regime-conditioned experts, achieving competitive performance and improvements ove…

The paper investigates forecasting sparse and bursty vulnerability sightings, concluding that traditional time-series models like SARIMAX are inadequate, and count-based methods like Poisson regressio…

This paper shows that the pricing of outcomes in prediction markets is significantly influenced by the financial friction of delayed settlement, quantifying this effect using an annualized settlement…

KairosAgent is a novel agentic framework that combines Large Language Models (LLMs) for semantic reasoning and Time Series Foundation Models (TSFMs) for numerical forecasting, achieving superior multi…

The paper proposes INTARG, an informed and selective adversarial attack framework for time-series forecasting that significantly increases prediction error by targeting only the most vulnerable time s…

The paper forecasts the Kalimati Vegetable Price Index (KVPI) using a Momentum-Corrected Online Stacking Ensemble, achieving high accuracy (RMSE=1.771, MAPE=0.68%) for long-term price predictions.

The paper introduces an LLM-agent framework to solve the 'last-mile forecasting' problem, bridging the gap between raw statistical predictions and business-ready forecasts by incorporating weakly stru…

The paper argues that long context windows are necessary for time series forecasting not just to capture long-range dependencies, but primarily to reduce uncertainty about the underlying data-generati…

This paper studies the problem of aggregating calibrated Bayesian experts into a new calibrated expert.

The paper proposes methodologies to measure lag relevance in machine learning forecasting models using Ghost variables, Shapley values, and additive importance measures. It also introduces auto-releva…

The paper introduces an explainable risk scoring framework that evaluates tokenized real-world assets (RWAs) based on liquidity, concentration, and market quality, demonstrating that total value locke…

This paper characterizes how estimation error in covariance matrices affects the global minimum-variance portfolio and derives a decision geometry for regret.

The paper proposes VLBM, a latent basis modeling framework, to achieve state-of-the-art robustness in multivariate time series forecasting, particularly when facing rare but high-impact out-of-distrib…

This paper derives the complete theory of covariance regret functional for decision making, providing insights on steepest-descent directions and boundary-optimal solutions.

This paper compares the performance of quantum kernels to classical ones in predicting stock returns on the Chinese A-share market and finds no quantum advantage.

The paper introduces ForesightFlow, an Information Leakage Score (ILS) framework, to quantify pre-event information leakage in prediction markets, and proposes a necessary extension to analyze empiric…

This paper develops a novel, computationally efficient method to quantify the uncertainty in wide neural network predictions by characterizing the limiting random fluctuations using stochastic evoluti…

AdaKoop introduces an efficient streaming algorithm that models complex nonlinear dynamics from nonstationary data streams by leveraging the Koopman operator theory, achieving state-of-the-art accurac…