mlsanityData to 14 Oct 2026

Time-Series Anomaly Detection with Implicit Neural Representation

arXiv 2201.11950 ↗First posted 28 Jan 2022cs.LGcited by 10

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Abstract (arXiv)

Detecting anomalies in multivariate time-series data is essential in many real-world applications. Recently, various deep learning-based approaches have shown considerable improvements in time-series anomaly detection. However, existing methods still have several limitations, such as long training time due to their complex model designs or costly tuning procedures to find optimal hyperparameters (e.g., sliding window length) for a given dataset. In our paper, we propose a novel method called Implicit Neural Representation-based Anomaly Detection (INRAD). Specifically, we train a simple multi-layer perceptron that takes time as input and outputs corresponding values at that time. Then we utilize the representation error as an anomaly score for detecting anomalies. Experiments on five real-world datasets demonstrate that our proposed method outperforms other state-of-the-art methods in performance, training speed, and robustness.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 14 of its 31 references that are in the map. First posted on arXiv 28 Jan 2022.

Builds on

14 of 31 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  3. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
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  5. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,232
  6. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
  7. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  8. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  9. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 224
  10. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
  11. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  12. 2020A review on outlier/anomaly detection in time series datacited by 189
2 more
  1. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  2. 2017Dilated Recurrent Neural Networkscited by 83

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Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 2; 2025: 3.

10 papers.

  1. 2022HyperTime: Implicit Neural Representation for Time Seriescited by 24
  2. 2022Learning Deep Time-index Models for Time Series Forecastingcited by 18
  3. 2023Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representationscited by 17
  4. 2024TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detectioncited by 5
  5. 2022An Anomaly Detection Method for Satellites Using Monte Carlo Dropoutcited by 4
  6. 2023MADS: Modulated Auto-Decoding SIREN for time series imputationcited by 3
  7. 2025ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Datacited by 3
  8. 2025Neural Functions for Learning Periodic Signalcited by 2
  9. 2024PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equationscited by 1
  10. 2025Temporal Variational Implicit Neural Representationscited by 1