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Detecting and Imputing Hidden Missing Values in Time Series Data: Case study: Alfa Laval
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology.
2024 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Although identifying missing values in regular time series is trivial,detecting them becomes a challenge with irregular timestamps. Toreduce the storage, our partner, Alfa Laval, uses an engineering trickto store measurements in time series databases only when their valuechanges. This solution, despite solving storage problems, can createproblems in data analysis. It also complicates the identification ofmissing values.

We address two problems: identifying hidden missing values fromirregular time series and developing effective imputation techniquesfor them. We use a rule-based approach to locate hidden missing val-ues tailored to the Alfa Laval dataset. Once we have identified the po-sition of hidden missing values, imputing them becomes the greaterchallenge, particularly when missing gaps are long. Our experimentsshow that while Linear Interpolation often outperforms LSTM andARIMA, it only creates a straight line between two points, failing tocapture the shape of the missing data. Consequently, in long-termgaps, we miss lots of informative fluctuations.

To address these limitations, we employ a pattern-based similar-ity search method, which effectively captures the value and shape oftime series data for more accurate imputation. This thesis presentsour novel approach, which we validate on a subset of Alfa Laval’ssensor data and three additional external datasets, demonstrating itsgeneralizability and effectiveness. While the rule-based identificationtechnique is particularly relevant to Alfa Laval’s data, our imputationtechnique serves as a general solution for time series imputation

Place, publisher, year, edition, pages
2024. , p. 112
Keywords [en]
hidden missing value, time series, pattern similarity search, similarity search, missing value imputation, time series
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hh:diva-54251OAI: oai:DiVA.org:hh-54251DiVA, id: diva2:1882792
External cooperation
Alfa Laval
Supervisors
Examiners
Available from: 2024-07-16 Created: 2024-07-07 Last updated: 2024-08-09Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf