6 papers
DyWPE: Signal-Aware Dynamic Wavelet Positional Encoding for Time Series Transformers
Habib Irani, Vangelis Metsis
Existing positional encoding methods in transformers are fundamentally signal-agnostic, deriving positional information solely from sequence indices while ignoring the underlying s…
Positional Encoding in Transformer-Based Time Series Models: A Survey
Habib Irani, Vangelis Metsis
Recent advancements in transformer-based models have greatly improved time series analysis, providing robust solutions for tasks such as forecasting, anomaly detection, and classif…
EnsAug: Augmentation-Driven Ensembles for Human Motion Sequence Analysis
Bikram De, Habib Irani, Vangelis Metsis
Data augmentation is a crucial technique for training robust deep learning models for human motion, where annotated datasets are often scarce. However, generic augmentation methods…
WaveFormer: Wavelet Embedding Transformer for Biomedical Signals
Habib Irani, Bikram De, Vangelis Metsis
Biomedical signal classification presents unique challenges due to long sequences, complex temporal dynamics, and multi-scale frequency patterns that are poorly captured by standar…
Time Series Embedding Methods for Classification Tasks: A Review
Habib Irani, Yasamin Ghahremani, Arshia Kermani +1
Time series analysis has become crucial in various fields, from engineering and finance to healthcare and social sciences. Due to their multidimensional nature, time series often n…
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG
Arshia Kermani, Veronica Perez-Rosas, Vangelis Metsis
This study presents a systematic comparison of three approaches for the analysis of mental health text using large language models (LLMs): prompt engineering, retrieval augmented g…