Autoencoder Based Iterative Modeling and Multivariate Time-Series
Subsequence Clustering Algorithm
- URL: http://arxiv.org/abs/2209.04213v1
- Date: Fri, 9 Sep 2022 09:59:56 GMT
- Title: Autoencoder Based Iterative Modeling and Multivariate Time-Series
Subsequence Clustering Algorithm
- Authors: Jonas K\"ohne, Lars Henning, Clemens G\"uhmann
- Abstract summary: This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient time-series data (MTSD)
We use a recurrent neural network (RNN) based Autoencoder (AE) which is iteratively trained on incoming data.
A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper introduces an algorithm for the detection of change-points and the
identification of the corresponding subsequences in transient multivariate
time-series data (MTSD). The analysis of such data has become more and more
important due to the increase of availability in many industrial fields.
Labeling, sorting or filtering highly transient measurement data for training
condition based maintenance (CbM) models is cumbersome and error-prone. For
some applications it can be sufficient to filter measurements by simple
thresholds or finding change-points based on changes in mean value and
variation. But a robust diagnosis of a component within a component group for
example, which has a complex non-linear correlation between multiple sensor
values, a simple approach would not be feasible. No meaningful and coherent
measurement data which could be used for training a CbM model would emerge.
Therefore, we introduce an algorithm which uses a recurrent neural network
(RNN) based Autoencoder (AE) which is iteratively trained on incoming data. The
scoring function uses the reconstruction error and latent space information. A
model of the identified subsequence is saved and used for recognition of
repeating subsequences as well as fast offline clustering. For evaluation, we
propose a new similarity measure based on the curvature for a more intuitive
time-series subsequence clustering metric. A comparison with seven other
state-of-the-art algorithms and eight datasets shows the capability and the
increased performance of our algorithm to cluster MTSD online and offline in
conjunction with mechatronic systems.
Related papers
- SMORE: Similarity-based Hyperdimensional Domain Adaptation for
Multi-Sensor Time Series Classification [17.052624039805856]
We propose SMORE, a novel resource-efficient domain adaptation (DA) algorithm for multi-sensor time series classification.
SMORE achieves on average 1.98% higher accuracy than state-of-the-art (SOTA) DNN-based DA algorithms with 18.81x faster training and 4.63x faster inference.
arXiv Detail & Related papers (2024-02-20T18:48:49Z) - Minimally Supervised Learning using Topological Projections in
Self-Organizing Maps [55.31182147885694]
We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs)
Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU)
Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
arXiv Detail & Related papers (2024-01-12T22:51:48Z) - An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks.
The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions.
We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
arXiv Detail & Related papers (2023-09-30T15:57:14Z) - Detection of Anomalies in Multivariate Time Series Using Ensemble
Techniques [3.2422067155309806]
We propose an ensemble technique that combines multiple base models toward the final decision.
A semi-supervised approach using a Logistic Regressor to combine the base models' outputs is also proposed.
The performance improvement in terms of anomaly detection accuracy reaches 2% for the unsupervised and at least 10% for the semi-supervised models.
arXiv Detail & Related papers (2023-08-06T17:51:22Z) - Custom DNN using Reward Modulated Inverted STDP Learning for Temporal
Pattern Recognition [0.0]
Temporal spike recognition plays a crucial role in various domains, including anomaly detection, keyword spotting and neuroscience.
This paper presents a novel algorithm for efficient temporal spike pattern recognition on sparse event series data.
arXiv Detail & Related papers (2023-07-15T18:57:27Z) - Multivariate Time Series Classification: A Deep Learning Approach [1.0742675209112622]
This paper investigates different methods and various neural network architectures applicable in the time series classification domain.
Data is obtained from a fleet of gas sensors that measure and track quantities such as oxygen and sound.
With the help of this data, we can detect events such as occupancy in a specific environment.
arXiv Detail & Related papers (2023-07-05T12:50:48Z) - MTS2Graph: Interpretable Multivariate Time Series Classification with
Temporal Evolving Graphs [1.1756822700775666]
We introduce a new framework for interpreting time series data by extracting and clustering the input representative patterns.
We run experiments on eight datasets of the UCR/UEA archive, along with HAR and PAM datasets.
arXiv Detail & Related papers (2023-06-06T16:24:27Z) - TELESTO: A Graph Neural Network Model for Anomaly Classification in
Cloud Services [77.454688257702]
Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance.
One direction aims at the recognition of re-occurring anomaly types to enable remediation automation.
We propose a method that is invariant to dimensionality changes of given data.
arXiv Detail & Related papers (2021-02-25T14:24:49Z) - Deep Cellular Recurrent Network for Efficient Analysis of Time-Series
Data with Spatial Information [52.635997570873194]
This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information.
The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
arXiv Detail & Related papers (2021-01-12T20:08:18Z) - Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge
Computing [113.52575069030192]
Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles.
Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center.
We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes.
A class of mini-batch alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.
arXiv Detail & Related papers (2020-10-02T10:41:59Z) - Change Point Detection in Time Series Data using Autoencoders with a
Time-Invariant Representation [69.34035527763916]
Change point detection (CPD) aims to locate abrupt property changes in time series data.
Recent CPD methods demonstrated the potential of using deep learning techniques, but often lack the ability to identify more subtle changes in the autocorrelation statistics of the signal.
We employ an autoencoder-based methodology with a novel loss function, through which the used autoencoders learn a partially time-invariant representation that is tailored for CPD.
arXiv Detail & Related papers (2020-08-21T15:03:21Z)
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.