21 citations · 58 across the 6 of their papers we have counts for
7 papers
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification. Recently, automated augment…
HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis
Cameron Shand, Richard Allmendinger, Julia Handl +2
Comprehensive benchmarking of clustering algorithms is rendered difficult by two key factors: (i)~the elusiveness of a unique mathematical definition of this unsupervised learning…
Augmenting transferred representations for stock classification
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Stock classification is a challenging task due to high levels of noise and volatility of stocks returns. In this paper we show that using transfer learning can help with this task,…
Evaluating data augmentation for financial time series classification
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their us…
A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing
Elizabeth Fons, Paula Dawson, Jeffrey Yau +2
The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institu…
Data Context Informed Data Wrangling
Martin Koehler, Alex Bogatu, Cristina Civili +6
The process of preparing potentially large and complex data sets for further analysis or manual examination is often called data wrangling. In classical warehousing environments, t…