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Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

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Related Experiment Video

Updated: Feb 26, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Multi-Time Attention Networks for Irregularly Sampled Time Series.

Satya Narayan Shukla¹, Benjamin M Marlin¹

¹College of Information and Computer Sciences University of Massachusetts Amherst, Amherst, MA 01003, USA.

... International Conference on Learning Representations

|February 25, 2026

Summary

This summary is machine-generated.

We introduce Multi-Time Attention Networks, a novel deep learning framework for irregularly sampled time series data. This approach achieves competitive or superior performance on interpolation and classification tasks with faster training.

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Published on: January 23, 2017

Area of Science:

Artificial Intelligence
Machine Learning
Deep Learning

Background:

Irregularly sampled time series data pose significant challenges for standard deep learning models.
Physiological data in electronic health records are often sparse, irregularly sampled, and multivariate.
Existing models struggle to effectively handle the complexities of such data.

Purpose of the Study:

To propose a novel deep learning framework, Multi-Time Attention Networks, for modeling irregularly sampled time series.
To address the challenges of sparsity, irregular sampling, and multivariate nature in physiological time series data.
To develop a model capable of learning continuous time embeddings and producing fixed-length representations.

Main Methods:

Developed the Multi-Time Attention Networks (MTAN) framework.
Incorporated an attention mechanism to handle variable numbers of observations.
Learned embeddings for continuous time values.
Evaluated performance on interpolation and classification tasks using multiple datasets.

Main Results:

The proposed Multi-Time Attention Networks demonstrate performance comparable to or exceeding baseline and recent models.
The framework achieves significantly faster training times compared to current state-of-the-art methods.
Effectively handles sparse, irregularly sampled, and multivariate time series data.

Conclusions:

Multi-Time Attention Networks offer an effective and efficient solution for deep learning on irregularly sampled time series.
The framework shows promise for applications in electronic health records and other domains with similar data characteristics.
MTAN provides a valuable advancement in time series modeling for challenging real-world datasets.