Intraday Functional PCA Forecasting of Cryptocurrency Returns
Journal of Forecasting, EarlyView.ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley +1 more source
DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data. [PDF]
Sensors (Basel), 2022 Lee J, Kim W.
europepmc +1 more source
Lost in Translation? Risk‐Adjusting RMSE for Economic Forecast Performance
Journal of Forecasting, EarlyView.ABSTRACT When used for parameter optimization and/or model selection, traditional mean squared error (MSE)–based measures of forecast accuracy often exhibit a weak or even negative correlation with the economic value of return forecasts measured by, for example, the Sharpe ratios of the resulting portfolios.
Lukas Salcher +2 more
wiley +1 more source
Federated Learning for Breast Cancer Classification: A Comparative Study of Aggregation Methods
InformationFederated Learning (FL) allows healthcare institutions to collaboratively develop machine learning models while safeguarding patient data, making it ideal for privacy-sensitive medical imaging.
Nadjat Saàdia Lachemi +2 more
doaj +1 more source
Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics. [PDF]
Sensors (Basel), 2023 Hsu HY +4 more
europepmc +1 more source
Using DSGE and Machine Learning to Forecast Public Debt for France
Journal of Forecasting, EarlyView.ABSTRACT Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and ...
Emmanouil Sofianos +4 more
wiley +1 more source
Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-iid) Data. [PDF]
IEEE Trans Pattern Anal Mach Intell, 2023 Nguyen KP, Treacher AH, Montillo AA.
europepmc +1 more source
Federated Loss Exploration for Improved Convergence on Non-IID Data
Internò C, Olhofer M, Jin Y, Hammer B. Federated Loss Exploration for Improved Convergence on Non-IID Data. In: 2024 International Joint Conference on Neural Networks (IJCNN). IEEE International Joint Conference on Neural Networks (IJCNN).
Hammer, Barbara ; https://orcid.org/ +3 more
core +1 more source
Evaluating Forecasts at Multiple Horizons: An Extension of the Diebold–Mariano Approach
Journal of Forecasting, EarlyView.ABSTRACT Forecast accuracy tests are fundamental tools for comparing competing predictive models. The widely used Diebold–Mariano (DM) test assesses whether differences in forecast errors are statistically significant. However, its standard form is limited to pairwise comparisons at a single forecast horizon.
Andrew Grant +2 more
wiley +1 more source
An Overview of Autonomous Connection Establishment Methods in Peer-to-Peer Deep Learning
IEEE AccessThe exchange of model parameters between peers is critical in peer-to-peer deep learning. Historically, connections between agents were assigned randomly based on network topology.
Robert Sajina +2 more
doaj +1 more source