A survey on long short-term memory networks for time series prediction

By Benjamin Lindemann, Timo Müller, Hannes Vietz, Nasser Jazdi, Michael Weyrich. Procedia CIRP, 2021

649 citations recorded by the source corpus.

Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been investigated intensively in recent years due to their ability to model and predict nonlinear time-variant system dynamics. The present paper delivers a comprehensive overview of existing LSTM cell derivatives and network architectures for time series prediction. A categorization in LSTM with optimized cell state representations and LSTM with interacting cell states is proposed. The investigated approaches are evaluated against defined requirements being relevant for an accurate time series prediction. These include short-term and long-term memory behavior, the ability for multimodal and multi-step ahead predictions and the according error propagation. Sequence-to-sequence networks with partially conditioning outperform the other approaches, such as bidirectional or associative networks, and are best suited to fulfill the requirements.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a7628f4eaaae73d5f5251c408615b01369acf8f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nada Sahlab
2. Benjamin Maschler
3. Michael Weyrich
4. Benjamin Lindemann

Citations in the corpus

• A Survey on Anomaly Detection for Technical Systems using LSTM Networks (2021, background): “LSTM networks are particularly suitable for modeling multivariate time series and time-variant systems [20].”