Summary of ANNO: A Time Series Annotation Tool to Evaluate Event Detection Algorithms

[sneha3799]

[sneha3799]

ANNO: A Time Series Annotation Tool to Evaluate Event Detection Algorithms

Paper : https://link.springer.com/chapter/10.1007/978-3-030-45718-1_5
Authors : Huchtkoetter, Jana and Reinhardt, Andreas and Hossain, Sakif
Booktitle : Simulation Science: Second International Workshop, SimScience 2019 Clausthal-Zellerfeld, May 8-10, 2019, Revised Selected Papers 2
Pages : 70--87
Year : 2020
Organization : Springer

Abstract: The research field of energy analytics is concerned with the collection and processing of data related to electrical power generation and consumption. Electricity consumption data can reveal information pertaining to the nature of underlying appliances, their mode of operation, and many other aspects. Sudden load changes, so-called events, constitute the principal source of information in such time series data, thus their reliable detection and interpretation is a prerequisite for accurate energy analytics. The development of event detection algorithms is, however, hampered due to the unavailability of comprehensive data sets that feature energy consumption time series with corresponding event annotations. We hence present ANNO, a tool to provide annotations to time series consumption data in a supervised fashion and use them for the development of energy analytics algorithms, in this work.

Brief Thoughts:
Utility: ANNO offers a robust tool for annotating time series data, particularly for event detection in energy analytics. It is unique in its ability to efficiently handle large datasets and support metadata-rich annotations, which is an improvement over comparable tools like TagAnomaly.
Strengths:
Handles high-sampling-rate data effectively.
Supports both new and existing datasets with flexible input/output formats.
Allows detailed annotation, including metadata such as device types and transition information.
Modular design simplifies dataset integration.
Limitations:
Annotation flexibility is still somewhat limited compared to potential needs (e.g., handling overlapping events or complex metadata structures).
Requires further testing and enhancements for broader usability (e.g., automatic event detection features).
Relevance: While ANNO is not directly an algorithm, its annotation capabilities are crucial for benchmarking CPD and event detection algorithms, particularly in NILM contexts.

Metadata:
Algorithm Name:
N/A (This is a tool, not an algorithm, but it supports event detection algorithms)
Literature References:
Original Paper: ANNO: A Time Series Annotation Tool to Evaluate Event Detection Algorithms
(Provide actual link if available)
Hyper-parameters:
N/A (Tool settings, not algorithm hyper-parameters)

Abstract Typing:
Type of Label/Detection:
Labels events as discrete points, not segments.
Segment Overlap:
No overlapping segments (strictly point-based annotations).
Label Deterministic/Probabilistic:
Deterministic (specific event markers like timestamps).
Label Meaning:
Event (device-specific or generic), includes optional metadata like device ID and mains phase.
Score Return:
No scores; focuses on annotation only.
Label Annotation:
Categorical (event type, device ID).
Learning Type:
Unsupervised: The tool annotates manually or semi-automatically but does not learn from data.
Learning Mode:
Batch (entire dataset loaded for annotation).
Univariate/Multivariate Capability:
Multivariate: Supports datasets with multiple types of measurements.
Time Series Scitype:
Single Time Series: Focuses on single-stream data but can handle diverse formats for different time series.

Other Information:
Implementation/Library:
Authors: Listed in the paper; details like GitHub profiles are not specified.
Repo URL: Unknown; likely not an open-source implementation.
Code Status: Appears to exist as a standalone Python tool but not distributed via PyPI or similar.
Language: Python.
License: Not specified in the paper.
Maintenance Status: Likely limited to research group; unclear if actively maintained.
Governance Model: No details provided.

Evaluation of Utility for sktime:
Usefulness:
ANNO itself does not implement CPD algorithms but offers annotation support, which is critical for algorithm benchmarking and evaluation.
Useful for creating labeled datasets for supervised CPD tasks.
Algorithms Worth Exploring:
While no direct algorithms are detailed, the tool supports evaluation and refinement of CPD algorithms, particularly for NILM tasks.
Implementation Suggestions:
Develop interoperability with ANNO’s output format (e.g., integrate annotated datasets into sktime workflows).
Leverage the modular data-loading approach in sktime for handling diverse dataset formats.