Results 201 to 210 of about 937,585 (275)

Visualizing Uncertainty in Time Series Forecasts: The Impact of Uncertainty Visualization on Users' Confidence, Algorithmic Advice Utilization, and Forecasting Performance

Journal of Forecasting, EarlyView.
ABSTRACT Time series forecasts are always associated with uncertainty. However, experimental studies on the impact of uncertainty communication provide inconclusive results on the effect of providing this uncertainty to end users. In this study, we examine the impact of uncertainty visualizations on advice utilization in the context of time series ...
Dirk Leffrang, Oliver Müller
wiley   +1 more source

Modeling the Implied Volatility Smirk in China: Do Non‐Affine Two‐Factor Stochastic Volatility Models Work?

Journal of Futures Markets, EarlyView.
ABSTRACT In this paper, we investigate alternative one‐factor and two‐factor continuous‐time models with both affine and non‐affine variance dynamics for the Chinese options market. Through extensive empirical analysis of the option panel fit and diagnostics, we find that it is necessary to include both the non‐affine feature and the multi‐factor ...
Yifan Ye, Zheqi Fan, Xinfeng Ruan
wiley   +1 more source

Region-based value iteration for partially observable Markov decision processes [PDF]

, 2006
Hui Li, Xuejun Liao, Lawrence Carin
openalex   +1 more source

Automatic voltage control considering demand response: Approximatively completed observed Markov decision process-based reinforcement learning scheme

International Journal of Electrical Power & Energy Systems
To fully utilize the voltage regulation capacity of flexible load and distributed generations (DGs), we propose a novel Approximatively Completed Observed Markov Decision Process-based (ACOMDP-based) Reinforcement Learning (RL) (namely, ACMRL) scheme for
Yaru Gu, Xueliang Huang
doaj  

Toward affective dialogue management using partially observable Markov decision processes [PDF]

, 2008
Trung Bui
openalex   +1 more source

Forecasting Digital Asset Return: An Application of Machine Learning Model

International Journal of Finance &Economics, EarlyView.
ABSTRACT In this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable.
Vito Ciciretti, Alberto Pallotta, Suman Lodh, P. K. Senyo, Monomita Nandy   +4 more
wiley   +1 more source

A Reversed Early Warning Methodology for Optimal Bank Profit Retention Recommendations

International Journal of Finance &Economics, EarlyView.
ABSTRACT This study introduces a calibration method for the newest policy instrument in prudential supervision by endogenising profit retention targets via a reversed early warning system, depending on the supervisors' risk tolerance, the exposure to the economy, and the level of financial pressure.
Petr Jakubik, Bogdan Gabriel Moinescu
wiley   +1 more source

Optimal Control of Logically Constrained Partially Observable and Multi-Agent Markov Decision Processes [PDF]

Krishna C. Kalagarla, Dhruva Kartik, Dong‐Ming Shen, Rahul Jain, Ashutosh Nayyar, Pierluigi Nuzzo   +5 more
openalex   +1 more source

Informational Differences, Adaptive Learning, and Inflation Forecast Bias

International Studies of Economics, EarlyView.
ABSTRACT This work highlights a previously overlooked factor that contributes to bias in private inflation forecasts—ignorance of confidential monetary rules. Additionally, it examines how this ignorance indirectly affects policy rate settings. The model proposed reconciles biases in two key forecast sources: the inflation expectations from the Survey ...
Qiang Chen, Zechen Yin
wiley   +1 more source

An Extension of Profit Sharing to Partially Observable Markov Decision Processes: Proposition of PS-r* and its Evaluation.

, 2003
Kazuteru Miyazaki, Shigenobu Kobayashi
openalex   +2 more sources