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Found 8 papers, 2 papers with code
Rough Transformers for Continuous and Efficient Time-Series Modelling
To mitigate this, we introduce the Rough Transformer, a variation of the Transformer model which operates on continuous-time representations of input sequences and incurs significantly reduced computational costs, critical for addressing long-range dependencies common in medical contexts.
Interpretable Spectral Variational AutoEncoder (ISVAE) for time series clustering
The best encoding is the one that is interpretable in nature.
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers
Essential to this choice is the fill probability of a passive limit order placed in the LOB.
Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data
Sleep constitutes a key indicator of human health, performance, and quality of life.
Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data
Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time, comprising a tool that helps clinicians supervise patients' evolution over time and enhance the associated treatments' outcomes.
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions
Volatility forecasts play a central role among equity risk measures.
PyHHMM: A Python Library for Heterogeneous Hidden Markov Models
We introduce PyHHMM, an object-oriented open-source Python implementation of Heterogeneous-Hidden Markov Models (HHMMs).
Deep Autoregressive Models with Spectral Attention
In this paper, we propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module, which merges global and local frequency domain information in the model's embedded space.
