Multivariate, machine learning models

The gated recurrent unit (GRU) model is a type of recurrent neural network. As such it is well suited for sequential data such as time series. The main strength is the high flexibility compared to linear models, a GRU model can identify non-linear patterns in data, allowing it to more accurately describe it. It is similar to LSTM but has fewer parameters, which lessens the risk of overfitting on smaller sets of data. GRU is trained on data using variants of gradient descent, such as AdaGrad and ADAM.

The long short-term memory (LSTM) model is an artificial recurrent neural network. It is especially suited for processing sequences of data, owing to its feedback connections. LSTM models are used for many different tasks such as speech and video analysis, as well as time series analysis. One of the main strengths of an LSTM model is its flexibility, it can identify complex structures in data thanks to its non-linear activation functions and heavy parametrization. LSTM is trained on data using variants of gradient descent, such as AdaGrad and ADAM.

The artificial neural network (ANN) is a model inspired by biological neural networks such as the human brain. The model is an example of a more sparse machine learning model compared to LSTM and GRU. This lessens the risk of overfitting while still offering more flexibility than a linear model. ANN is trained on data using variants of gradient descent, such as AdaGrad and ADAM.