Background Research - Review and Survey Papers

[RobotPsychologist]

Background Research

Completion Deadline: November 20th, 2024

The sktime sibling issue to this one is sktime/sktime#6481, we can add our desired annotation algorithms to that issue as we go.

The lists below are the most heavily cited recent survey papers that may be relevant to our project. After the meta-review, identify newer techniques that reference these survey papers.

Final Deliverable

A prioritized list documented in a markdown file in 0_meal_identification/meal_identification/references

You should record the Abstract Typing, and Metadata for each paper so that the sktime-dev team can properly tag the algorithm in the registry. If you're pressed for time the most important information the, name of the algorithm, literature references, and Abstract Typing.

Sub Deliverables: Create a markdown file for each category of papers below for each paper in a markdown file:

• create a brief summary of your thoughts on the paper,
• what is useful to us, or is it not useful at all
• which algorithms are worth trying, a link to the original paper of the identified algorithm we may want to implement.

Metadata

• name of algorithm
• literature references
• what are hyper-parameters
  • clear from paper yes/no
  • if yes: name and type if possible
  • this can be done after prioritization as this is often complicated (obscure/unclear, requires an understanding of math and algos on formal level)

Abstract Typing

• type of label/detection: points, segments, both, something else
  • segment = (start time stamp, end time stamp)
  • if segments, can they overlap
  • are labels deterministic, or probabilistic (probability of segment)
• type of label meaning: outlier/anomaly, changepoint, mixed/multiple, sth else
• does the algorithm also return a score, e.g., anomaly score
• label annotation present? If yes: categorical, numerical
• learning type: supervised, unsupervised, semi-supervised
• learning mode: stream, batch, both
• univariate only or multivariate capability
• time series scitype: single time series, panel/collection, hierarchical, multiple of these, sth else

Metrics will have the same dimensions (except perhaps a few), I'll put metrics in a different issue.

Other Information

Implementation/library:

For the algorithms that already have a well-developed implementation or library:

• authors (GitHub names etc)
• repo URL if applicable
• pypi name(s) if applicable
• code status: pypi package, contained in package, loose code no package, only paper (no code)
• language: python, R, julia, sth else
• license (type): copyleft, permissive, copyrighted
• maintenance status: still maintained, no longer maintained, unclear
• governance model, who owns/controls, key points of contact (e.g., maintainers, company)

Packages That Contain Detectors

They are both active and defunct that contain detectors (without the typing typically!)

• ruptures
• adtk
  • Arundo toolbox - is that the same?
• gluon-ts detection module
• seqlearn
• seglearn (one of the two has detectors but I forgot which)
• darts detection module
• pyod, but that is tabular (non-temporal)
• hmmlearn
  • can do supervised and unsupervised, single series and panel/collection
  • but sktime has only one interfaced
  • and hmmlearn provides only variants of one major algorithm
• Turing change point detection benchmark
  • does not have cleanly delineated algorithms, but a larger list of names
  • snippets of code many of which are not reusable in multiple languages
• numenta benchmark

Papers

Change Point Detection

• A survey of methods for time series change point detection @sneha3799 #169
• Research - An Evaluation of Change Point Detection Algorithms @bekahma #168
• Selective review of offline change point detection methods @RobotPsychologist #138

Annotation

• - ANNO: A Time Series Annotation Tool to Evaluate Event Detection Algorithms @sneha3799

Segmentation

• - Unsupervised Time Series Segmentation: A Survey on Recent Advances @RobotPsychologist

Clustering

• - Time-series clustering – A decade review

Anomaly Detection

• - A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives @bekahma
• - A Review on Outlier/Anomaly Detection in Time Series Data @bekahma
• - Anomaly Detection for IoT Time-Series Data: A Survey @RobotPsychologist
• - Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress
• - Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines @walkerpayne
• - Deep Learning for Time Series Anomaly Detection: A Survey @sneha3799

Benchmarks

For these benchmarks, assess which are most similar to our meal detection problem, and then see which algorithms are currently performing best on the most appropriate benchmarks to include in sktime.

Anomaly Detection

• Papers with Code - Time Series Anomaly Detection
  • UCR
  • SMAP
  • SMD
  • SWaT
  • WADI
  • KPI
  • Yahoo A1
  • MSL
• Numenta Anomaly Benchmark

Change Point Detection

• [Papers with Code - Change Point Detection]) #163
  • SKAB #164
  • TEP #165
  • TSSB #166
• Turing Change Point Detection Benchmark #167

Diabetes Specific Meal Detection

• Automated meal detection from continuous glucose monitor data through simulation and explanation
• Data-driven meal events detection using blood glucose response patterns
• A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems
• Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review #173

[walkerpayne]

👋

[bekahma]

:D

[sneha3799]

:)

[jogong2718]

hi

[Cristianofiliped]

hi!

[bekahma]

A Review on Outlier/Anomaly Detection in Time Series Data
A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
An Evaluation of Change Point Detection Algorithms

I can also do this if I have time, but preference towards first 3:
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines

[walkerpayne]

@bekahma I'll snag Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines from you, if that's cool!

[sneha3799]

A survey of methods for time series change point detection
ANNO: A Time Series Annotation Tool to Evaluate Event Detection Algorithms
Deep Learning for Time Series Anomaly Detection: A Survey

[RobotPsychologist]

@fkiraly please let me know if you have any other survey papers worth adding.