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Found 12 papers, 1 papers with code
A Language Model-Guided Framework for Mining Time Series with Distributional Shifts
Effective utilization of time series data is often constrained by the scarcity of data quantity that reflects complex dynamics, especially under the condition of distributional shifts.
Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark
Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more.
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Augment on Manifold: Mixup Regularization with UMAP
Evaluations across diverse regression tasks show that UMAP Mixup is competitive with or outperforms other Mixup variants, show promise for its potential as an effective tool for enhancing the generalization performance of deep learning models.
Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections
Moreover, the required storage, computational time, and retrieval complexity to search in the time-series space are often non-trivial.
MADS: Modulated Auto-Decoding SIREN for time series imputation
Time series imputation remains a significant challenge across many fields due to the potentially significant variability in the type of data being modelled.
Deep Gaussian Mixture Ensembles
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty.
HyperTime: Implicit Neural Representation for Time Series
We show how these networks can be leveraged for the imputation of time series, with applications on both univariate and multivariate data.
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.
