# Sequence Models

Sequence models enable various sequence predictions that are inherent different to other more traditional predictive modeling techniques or supervised learning approaches. **In contrast to mathematical sets often used, the ‘sequence‘ model imposes an explicit order on the input/output data that needs to be preserved in training and/or inference.** These models are driven by application goals and include sequence prediction, sequence classification, sequence generation, and sequence-to-sequence prediction. All these models can be developed by using so-called Long Short-Term Memory (LSTM) models. Please refer to our article on a LSTM Neural Network for more pieces of information.

**The above approach of model categorization is typically based on different inputs/outputs to/from the sequence models.** This is best explained via a practical ‘standard dataset’ perspective. In this perspective, the order of samples is not important and training/testing datasets and their samples have often no explicit order. Hence they are rather typical ‘mathematical sets’. In contrast looking via a practical ‘sequence dataset‘ perspective, it becomes clear that the order of samples is important. In this perspective on the datasets, the sequence model learning/inference needs this order.

## Sequence Models Details

Have a look at the following video with more details: