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Advances in Intelligent Data Analysis XVIII: 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings

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Missing data is a common occurrence in the time series domain, for instance due to faulty sensors, server downtime or patients not attending their scheduled appointments. One of the best methods to impute these missing values is Multiple Imputations by Chained Equations (MICE) which has the drawback that it can only model linear relationships among the variables in a multivariate time series. The advancement of deep learning and its ability to model non-linear relationships among variables make it a promising candidate for time series imputation. This work proposes a modified Convolutional Denoising Autoencoder (CDA) based approach to impute multivariate time series data in combination with a preprocessing step that encodes time series data into 2D images using Gramian Angular Summation Field (GASF). We compare our approach against a standard feed-forward Multi Layer Perceptron (MLP) and MICE. All our experiments were performed on 5 UEA MTSC multivariate time series datasets, where 20 to 50% of the data was simulated to be missing completely at random. The CDA model outperforms all the other models in 4 out of 5 datasets and is tied for the best algorithm in the remaining case.

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Detail Information

Series Title

Lecture Notes in Computer Science

Call Number

005.74 AID r

Publisher

Cham : Springer Cham., 2020

Collation

XIV, 588p

Language

English

ISBN/ISSN

9783030445843

Classification

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Content Type

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Media Type

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Carrier Type

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Edition

1

Subject(s)

Education
Open Access
Artificial intelligence
Engineering
Computer vision
Data mining
Semantics
Graph theory
Social networks
learning systems
classification
clustering
learning algortihms
supervised learning
association rules
graphic methods
neural networks
correlation analysis
databases
image analysis

Specific Detail Info

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Statement of Responsibility

edit by R. Berthold Michael [at.al.]

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