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Found 4 papers, 1 papers with code
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation
Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification.
Evaluating data augmentation for financial time series classification
Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their use in time series classification is still at an early stage.
Augmenting transferred representations for stock classification
In this paper we show that using transfer learning can help with this task, by pre-training a model to extract universal features on the full universe of stocks of the S$\&$P500 index and then transferring it to another model to directly learn a trading rule.
A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing
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 institutional investors.
