Modelling Irregularly Sampled Time Series Without Imputation
- URL: http://arxiv.org/abs/2309.08698v1
- Date: Fri, 15 Sep 2023 18:43:41 GMT
- Title: Modelling Irregularly Sampled Time Series Without Imputation
- Authors: Rohit Agarwal, Aman Sinha, Dilip K. Prasad, Marianne Clausel,
Alexander Horsch, Mathieu Constant and Xavier Coubez
- Abstract summary: Modelling irregularly-sampled time series (ISTS) is challenging because of missing values.
Most existing methods focus on handling ISTS by converting irregularly sampled data into regularly sampled data via imputation.
We present SLAN, which utilizes a pack of LSTMs to model ISTS without imputation, eliminating the assumption of any underlying process.
- Score: 44.79338100842851
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modelling irregularly-sampled time series (ISTS) is challenging because of
missing values. Most existing methods focus on handling ISTS by converting
irregularly sampled data into regularly sampled data via imputation. These
models assume an underlying missing mechanism leading to unwanted bias and
sub-optimal performance. We present SLAN (Switch LSTM Aggregate Network), which
utilizes a pack of LSTMs to model ISTS without imputation, eliminating the
assumption of any underlying process. It dynamically adapts its architecture on
the fly based on the measured sensors. SLAN exploits the irregularity
information to capture each sensor's local summary explicitly and maintains a
global summary state throughout the observational period. We demonstrate the
efficacy of SLAN on publicly available datasets, namely, MIMIC-III, Physionet
2012 and Physionet 2019. The code is available at
https://github.com/Rohit102497/SLAN.
Related papers
- PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
We propose a.
Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns.
We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
arXiv Detail & Related papers (2024-06-04T13:51:08Z) - Continuous time recurrent neural networks: overview and application to
forecasting blood glucose in the intensive care unit [56.801856519460465]
Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations.
We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
arXiv Detail & Related papers (2023-04-14T09:39:06Z) - TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional
Cycle-Consistent Generative Adversarial Networks [2.4469484645516837]
Anomaly detection is widely used in network intrusion detection, autonomous driving, medical diagnosis, credit card frauds, etc.
This paper proposes TSI-GAN, an unsupervised anomaly detection model for time-series that can learn complex temporal patterns automatically.
We evaluate TSI-GAN using 250 well-curated and harder-than-usual datasets and compare with 8 state-of-the-art baseline methods.
arXiv Detail & Related papers (2023-03-22T23:24:47Z) - STING: Self-attention based Time-series Imputation Networks using GAN [4.052758394413726]
STING (Self-attention based Time-series Imputation Networks using GAN) is proposed.
We take advantage of generative adversarial networks and bidirectional recurrent neural networks to learn latent representations of the time series.
Experimental results on three real-world datasets demonstrate that STING outperforms the existing state-of-the-art methods in terms of imputation accuracy.
arXiv Detail & Related papers (2022-09-22T06:06:56Z) - Imputing Missing Observations with Time Sliced Synthetic Minority
Oversampling Technique [0.3973560285628012]
We present a simple yet novel time series imputation technique with the goal of constructing an irregular time series that is uniform across every sample in a data set.
We fix a grid defined by the midpoints of non-overlapping bins (dubbed "slices") of observation times and ensure that each sample has values for all of the features at that given time.
This allows one to both impute fully missing observations to allow uniform time series classification across the entire data and, in special cases, to impute individually missing features.
arXiv Detail & Related papers (2022-01-14T19:23:24Z) - Networked Time Series Prediction with Incomplete Data [59.45358694862176]
We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future.
We conduct extensive experiments on three real-world datasets under different missing patterns and missing rates.
arXiv Detail & Related papers (2021-10-05T18:20:42Z) - "Forget" the Forget Gate: Estimating Anomalies in Videos using
Self-contained Long Short-Term Memory Networks [20.211951213040937]
We present an approach of detecting anomalies in videos by learning a novel LSTM based self-contained network on normal dense optical flow.
We introduce a bi-gated, light LSTM cell by discarding the forget gate and introducing sigmoid activation.
Removing the forget gate results in a simplified and undemanding LSTM cell with improved performance effectiveness and computational efficiency.
arXiv Detail & Related papers (2021-04-03T20:43:49Z) - Learning summary features of time series for likelihood free inference [93.08098361687722]
We present a data-driven strategy for automatically learning summary features from time series data.
Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
arXiv Detail & Related papers (2020-12-04T19:21:37Z) - Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing
Valued Time-Series Data Using LSTM Networks [0.0]
We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values.
Our algorithm is fully unsupervised, however, can be readily extended to supervised or semisupervised cases.
arXiv Detail & Related papers (2020-05-25T09:41:04Z) - Contextual-Bandit Anomaly Detection for IoT Data in Distributed
Hierarchical Edge Computing [65.78881372074983]
IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay.
We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems.
We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
arXiv Detail & Related papers (2020-04-15T06:13:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.