# Anticipating Future Behavior of an Industrial Press Using LSTM Networks
## 2 Related Work
### Prediction with LSTM Models  
LSTM neural networks achieved the best performance in a number of computational sequence labeling tasks, including speech recognition and machine translation [26]. There are a variety of engineering problems that can be solved using predictive neural models. [PERSON] used neural network models to predict problems of suspended road bridge structures based on global navigation satellite system observations [27]. [PERSON] et al. demonstrated that the proposed LSTM architectures exhibit better performances compared to deep neural networks (DNNs) in a large vocabulary speech recognition task with a large number of output states [28]. [PERSON] et al. adopted LSTMs for predicting the failure of heavy truck air compressors [29]. They concluded that the use of LSTMs leads to more consistency in predictions over time compared to models that ignore history, such as random forest models.  
[PERSON] et al. [30] presented an extension that they called Contextual LSTM (CLSTM). This model was also used for the forecasting of pollutants. There is also the proposal for a genetic long short-term memory (GLSTM), which has been used in the study of wind energy forecasting [31]. [PERSON] et al. presented a combination method based on real-time prediction errors in which the support vector regression (SVR) and LSTM outputs are combined in the final results of the model's prediction, thus obtaining results of greater precision [32].  
[PERSON] et al. used a combination of a Convolution Neural Networks (CNNs) and LSTM in order to extract more in-depth information from data to predict the useful life of ion batteries [33]. [PERSON] et al. used an LSTM and developed an effective speed prediction model to solve prediction problems over time [34]. [PERSON] et al. report that the LSTM algorithm is superior and, according to them, it performs better than conventional neural network models [35].  
The architecture of an LSTM network includes the number of hidden layers and the number of delay units, which is the number of previous data points that are considered for training and testing. Currently, there is no general rule for selecting the number of delays and hidden layers [36]. A deep LSTM can be built by stacking multiple LSTM layers, which generally works better than a single layer. Deep LSTM networks have been applied to solve many real-world sequence modelling problems [37]. The LSTM can also be used for planning studies [38], namely for planning the analysis of road traffic speed.  
To produce a prediction model with good accuracy, it is necessary to optimize neural models' hyperparameters. While simple models can often produce good results with default hyperparemeters, the optimization process can greatly improve the results [39; 40; 41]. The selection of hyperparameters often makes the difference between underperformance and state-of-the-art performance. Optimization is often performed using machine learning algorithms, such as grid search, grey wolf optimization or particle swarm optimization. In the present prediction model, however, the hyperparameters were optimized manually, following a trial and error guided process, one variable at a time. This method was followed because it was the most convenient considering the limited computing power available.