Time Series Anomaly Detection System with Linear Neural Network and Autoencoder

The methodical compliances of time series data have been acclimated for digitalization in ultramodern industrial processes. Artificial implanted detectors produce unknown streaming data which is available for dimensional reduction. The proposed reduction step exploits autoencoders infrequently combined with direct neural networks. The decentralized machine learning based monitoring detectors are equipped to capture bitsy changes in the product schedule. The monitoring detectors descry multi-dimensional changes in the product outfit of N-dimensional time series. The traditional anomaly detection methods are changeable in real-time industrial-based time series data. We propose a multidimensional, effective, and decentralized machine learning based anomaly detection system for industrial high dimensional product schedules in this paper. The experimental setup is designed to map multidimensional reduction from time series data. The proposed decentralized system improves calculation time and effective anomaly detection can be employed for processing real- time industrial data.