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Full-Text Articles in Longitudinal Data Analysis and Time Series
An Empirical Evaluation Of Neural Process Meta-Learners For Financial Forecasting, Kevin G. Patel
An Empirical Evaluation Of Neural Process Meta-Learners For Financial Forecasting, Kevin G. Patel
Master's Theses
Challenges of financial forecasting, such as a dearth of independent samples and non- stationary underlying process, limit the relevance of conventional machine learning towards financial forecasting. Meta-learning approaches alleviate some of these is- sues by allowing the model to generalize across unrelated or loosely related tasks with few observations per task. The neural process family achieves this by con- ditioning forecasts based on a supplied context set at test time. Despite promise, meta-learning approaches remain underutilized in finance. To our knowledge, ours is the first application of neural processes to realized volatility (RV) forecasting and financial forecasting in general.
We …
The Acquisition And Analysis Of Electroencephalogram Data For The Classification Of Benign Partial Epilepsy Of Childhood With Centrotemporal Spikes, Jessica A. Scarborough
The Acquisition And Analysis Of Electroencephalogram Data For The Classification Of Benign Partial Epilepsy Of Childhood With Centrotemporal Spikes, Jessica A. Scarborough
Master's Theses
In this thesis, I will expand upon each step in the process of acquiring and analyzing electroencephalogram (EEG) for the classification of benign childhood epilepsy with centrotemporal spikes. Despite huge advancements in the field of health informatics—natural language processing, machine learning, predictive modeling—there are significant barriers to the access of clinical data. These barriers include information blocking, privacy policy concerns, and a lack of stakeholder support. We will see that these roadblocks are all responsible for stunting biomedical research in some way, including my own experiences in acquiring the data for the second chapter of this thesis.
This second chapter …