DeepAI AI Chat
Log In Sign Up

Set Functions for Time Series

09/26/2019
by   Max Horn, et al.
0

Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that occur in many real-world datasets, such as healthcare applications. This paper proposes a novel framework for classifying irregularly sampled time series with unaligned measurements, focusing on high scalability and data efficiency. Our method SEFT (Set Functions for Time Series) is based on recent advances in differentiable set function learning, extremely parallelizable, and scales well to very large datasets and online monitoring scenarios. We extensively compare our method to competitors on multiple healthcare time series datasets and show that it performs competitively whilst significantly reducing runtime.

READ FULL TEXT

page 1

page 2

page 3

page 4

08/24/2022

DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series

Asynchronous Time Series is a multivariate time series where all the cha...
09/19/2019

Timage -- A Robust Time Series Classification Pipeline

Time series are series of values ordered by time. This kind of data can ...
08/17/2020

Learning from Irregularly-Sampled Time Series: A Missing Data Perspective

Irregularly-sampled time series occur in many domains including healthca...
10/05/2022

Tripletformer for Probabilistic Interpolation of Asynchronous Time Series

Asynchronous time series are often observed in several applications such...
05/05/2021

Granger Causality: A Review and Recent Advances

Introduced more than a half century ago, Granger causality has become a ...
11/24/2021

tsflex: flexible time series processing feature extraction

Time series processing and feature extraction are crucial and time-inten...
10/30/2019

Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries

This paper presents an approach to analyzing two-dimensional temporal da...