# Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters

arXiv: https://arxiv.org/abs/2301.00014 · HTML: https://mlsanity.com/paper/2301.00014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 30 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Real-Time Anomaly Detection for Streaming Analytics](https://mlsanity.com/paper/1607.02480/) (2016)

## Abstract (arXiv)

An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM). The system relies on machine learning algorithms for time series forecasting, historical data have been used to train a model and to predict the behavior of a sensor and, thus, to detect anomalies.