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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 …