Generative models may face limitations in capturing long-term dependencies and rare events in transactional data due to the following reasons:
Data Sparsity: Generative models may struggle to learn rare events or long-term dependencies if the data is sparse or imbalanced. The model may not have enough examples to effectively capture the patterns associated with these events.
Sequence Length: Long-term dependencies require the model to retain information over extended sequences, which can be challenging for some generative architectures. Short-term memory limitations may hinder the model's ability to capture long-term dependencies effectively.
Complexity: Rare events often have complex patterns that may not be adequately captured by the generative model's architecture. The model may prioritize more frequent events, leading to limited representation of rare occurrences.
Training Data: The quality and diversity of the training data can impact the generative model's ability to capture long-term dependencies and rare events. If the training data does not adequately represent these scenarios, the model may struggle to learn and generalize effectively.
By addressing these limitations through techniques such as data augmentation, specialized loss functions, and model architecture modifications, generative models can improve their ability to capture long-term dependencies and rare events in transactional data.