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Computer vision products require more validation time. Hardware integrations may slow early revenue, but $100K+ deals at 3yr are common.

Talent Scout

H

Hyeongwon Kang

Korea University

J

Jinwoo Park

Seoul National University

S

Seunghun Han

LG CNS

P

Pilsung Kang

Seoul National University

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Anomaly experts on LinkedIn & GitHub

References (43)

[1]
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
2025Minji Park, Won-Jeong Lee et al.
[2]
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast
2024Shiyan Hu, Kai Zhao et al.
[3]
Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling
2024Sinong Zhao, Wenrui Wang et al.
[4]
Transformer-based multivariate time series anomaly detection using inter-variable attention mechanism
2024H. Kang, Pilsung Kang
[5]
Precursor-of-Anomaly Detection for Irregular Time Series
2023Sheo Yon Jhin, Jaehoon Lee et al.
[6]
Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series
2023Sondre Sørbø, M. Ruocco
[7]
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
2023Yunhao Zhang, Junchi Yan
[8]
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
2022Yuqi Nie, Nam H. Nguyen et al.
[9]
Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression
2022Levente Foldesi, Matias Valdenegro-Toro
[10]
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
2022Haixu Wu, Teng Hu et al.
[11]
Are Transformers Effective for Time Series Forecasting?
2022Ailing Zeng, Mu-Hwa Chen et al.
[12]
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
2022Tian Zhou, Ziqing Ma et al.
[13]
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
2022Gerald Woo, Chenghao Liu et al.
[14]
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
2022Tian Zhou, Ziqing Ma et al.
[15]
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
2022Shizhan Liu, Hang Yu et al.
[16]
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
2022Taesung Kim, Jinhee Kim et al.
[17]
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
2021Jiehui Xu, Haixu Wu et al.
[18]
Towards a Rigorous Evaluation of Time-series Anomaly Detection
2021Siwon Kim, K. Choi et al.
[19]
Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization
2021Ahmed Abdulaal, Zhuanghua Liu et al.
[20]
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
2021Haixu Wu, Jiehui Xu et al.

Showing 20 of 43 references

Founder's Pitch

"FATE detects an early warning signs of anomalies in time-series data using uncertainty-aware ensemble forecasting."

Anomaly DetectionScore: 5View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

4/4 signals

10

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Why It Matters

Detecting anomalies early in time-series data can prevent significant losses in critical fields like manufacturing and finance by enabling proactive responses to potential failures before they manifest.

Product Angle

This can be productized as a SaaS platform for predictive maintenance across industries with heavy reliance on machinery, offering a dashboard with real-time alerts and analytics.

Disruption

This approach could potentially replace standard post-failure detection systems, offering a new proactive model of anomaly management that doesn't require labeled data for machine learning.

Product Opportunity

The market for predictive maintenance solutions is large, driven by industries like manufacturing, logistics, and energy that face costly downtime issues. Companies in these sectors are willing to pay for technologies that reduce failures and improve efficiency.

Use Case Idea

Develop a real-time monitoring solution for industrial equipment that predicts failures before they occur, reducing downtime and maintenance costs.

Science

The paper introduces FATE, an ensemble forecasting system that predicts future anomalies in time-series data without needing labeled anomaly data. It uses the variance in predictions from different models to assess uncertainty, signaling potential anomalies by leveraging ensemble disagreements to identify precursors of anomalies.

Method & Eval

FATE was tested against five benchmark datasets and outperformed traditional methods by 19-35% in detecting anomalies early, using the novel PTaPR metric to evaluate the timeliness and accuracy of predictions.

Caveats

Reliability can depend on the choice and diversity of ensemble models used. False positives in anomaly detection may lead to unnecessary interventions, requiring careful configuration and tuning for different applications.

Author Intelligence

Hyeongwon Kang

Korea University
hyeongwon kang@korea.ac.kr

Jinwoo Park

Seoul National University
jinwoo park@snu.ac.kr

Seunghun Han

LG CNS
seunghun.han@lgcns.com

Pilsung Kang

Seoul National University
pilsung kang@snu.ac.kr