# Novelty Detection in Time Series via Weak Innovations Representation: A Deep Learning Approach

arXiv: https://arxiv.org/abs/2210.13358 · HTML: https://mlsanity.com/paper/2210.13358/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Learning Independent Features with Adversarial Nets for Non-linear ICA](https://mlsanity.com/paper/1710.05050/) (2017)
- [Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection](https://mlsanity.com/paper/2106.12382/) (2021)
- [Normalizing flows for novelty detection in industrial time series data](https://mlsanity.com/paper/1906.06904/) (2019)
- [Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage](https://mlsanity.com/paper/1602.02265/) (2016)

## Built on by (most cited first, 1 of 1)

- [AI Foundation Model for Time Series with Innovations Representation](https://mlsanity.com/paper/2510.01560/) (2025)

## Abstract (arXiv)

We consider novelty detection in time series with unknown and nonparametric probability structures. A deep learning approach is proposed to causally extract an innovations sequence consisting of novelty samples statistically independent of all past samples of the time series. A novelty detection algorithm is developed for the online detection of novel changes in the probability structure in the innovations sequence. A minimax optimality under a Bayes risk measure is established for the proposed novelty detection method, and its robustness and efficacy are demonstrated in experiments using real and synthetic datasets.