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SCIENCE PASSION TECHNOLOGY

The PRESENT Project

PhD Talk

Julian Rakuschek

28.01.2025

Agenda

Introduction to PRESENT
Vibrana - Vibration Analysis
AnoScout - Anomalies in Time Series
PredictPal - Forecasting Time Series

Project PRESENT

PREdictions for Science, Engineering N' Technology

Funded by
FFG Austria

Coordinated by
Fraunhofer Austria

Forecasts and analyses of time series

Research in the field of visual analytics

Goal: A toolbox for tasks in the analysis of time series in three application areas (buildings, health, production)

How does it work?

Industry delivers data and problems

We develop prototypes

Sometimes something useful comes out

A beautiful synergy

The Consortium - Industry Meets Science

Scientific Consortium

Use Case: Production

Use Case: Health

Use Case: Buildings

Ethics and Integration

Our Approach

Design Triangle Approach

S. Miksch et al. "A matter of time: Applying a data-users-tasks design triangle to visual analytics of time-oriented data" Comput. Graph. 38 (2014): 286-290

Our Time Series Sources

Medical

Production

Facility

Time Series Tasks

Forecasting

Anomaly Detection

Drift Detection

Classification

Segmentation

Vibration Analysis

Goal: A Tool-Driven Decision Tree

Summary - What do we do?

  • Our Data Domains
    • Production
    • Building Management
    • Healthcare
  • Our Research Targets
    • Statistical and AI-based Methods
    • Interactive Data Visualization
  • Results
    • Decision Tree for Method Selection
    • Toolbox for Visualizations
    • Happy industry partners :)

Vibrana

Analysing Vibrations with Style

The Problem with Vibrations

Can you tell the difference?

A hidden signal

You cannot see the hidden signal with a line chart.

The Idea: Vibration $\rightarrow$ Point Cloud

The Time Delay Embedding

Only two lines of code!


windows = sliding_window_view(values, window_shape=window_size)
projected = PCA(n_components=2).fit_transform(windows)

Noise is not exciting ...

... but oscillations result in circles!

The effect of the window size

Grounded in Theory

Sliding Windows and Persistence: An Application of Topological Methods to Signal Analysis

Jose Perea and John Harer

Published by two professors of Duke University in the Foundations of Computational Mathematics journal.

They explained: Why the circles?

We asked: What can we do with this?

  1. Visualise change points
  2. Cluster signals
  3. Find labels

Our Prototype

Vibrana

EuroVis 2025 Submission

In Review 🍀

Can we find a change point in the signal?

Vibrations of a hydropower plant:

The TDE is a fingerprint evolving over time!

We can also cluster these fingerprints!

For the curious: We cluster by radius distribution

Applications

Engines

When can we detect wear?

Bearings

Which are faulty?

The Three-Charts-View

Simply Brush the Scatterplot

The labels are used for a similarity search

This reveals a creeping damage in the engine!

Finally: What about noise?

The most important insight however ...

... is that research is not always linear!

Small Cliffhanger

What if we have 75 TB of data? 🤯

To be continued ...

Anomalies

Anomalies in time series (Selection)

Check every time series by hand?

Let algorithms do the work!

Introducing AnoScout

A "sandbox" to check which algorithms work well and for exploring anomalies in the dataset.

Anomalies are represented as cards:

Manual Inspection

In the manual inspection interface, users may explore how different algorithms performed to detect a particular anomaly. Each algorithm output is a scoring - a time series where higher values correspond to anomalies in the data.

Exploring Anomalies

Exploring anomalies is achieved through a linked view, users may further provide feedback on the importance of an anomaly to satisfy a specific information need.

How do we gain an overview of all clusters?

Clustering!

Clustering Anomalies

The anomalies can be arranged by similarity in the scatterplot, such that similar anomalies are grouped together. This enables the user to discover patterns in the dataset.

Main features of AnoScout summarized

  1. Exploration pipeline for anomalies in time-oriented data.
  2. 7 algorithms for computing anomalies.
  3. "Playground" for testing various algorithms.
  4. Using user labels to fine-tune the system.

Application Scenario

  • A company wants to install a new machine.
  • The machine conducts an etching process (semiconductor manufacturing).
  • Each etching process is recorded through a sensor (e.g. pressure, temperature, and gas)
  • We want to use AnoScout to:
    1. Find possible anomaly patterns.
    2. Check which algorithms work well.

The Plan

Short Paper Track

Forecasting

How can we build a "sandbox" to explore forecasting models?

Introducing PredictPal

Just like AnoScout, but for Forecasting.

Prediction Models

ARIMA

AutoRegressive Integrated Moving Average

VAR

Vector Autoregression

Analysis View

The user selects a subset of the time series for training the prediction models and can immediately verify the model based on the visualization.

History of Models

The history keeps track of models found in the past.

A municipal office worker John Doe needs to predict the traffic load at Intersection X

The video shows the workflow of using PredictPal - first, we upload a dataset, then run statistical tests to ensure applicability of ARIMA and VAR. Next, the user tries various subsets of the time series and seasonilty configurations to arrive at a suitable model.

Towards XAI: ShapTime

Thank you!

Slides