Long-term dependency

Information

• In machine learning and artificial neural networks, long-term dependency refers to the challenge of capturing relationships between input and output variables that are separated by a significant time gap.
• Long-term dependencies can be particularly important in time series forecasting and natural language processing tasks, where the input and output sequences can be very long and complex.
• Traditional feedforward neural networks can struggle to capture long-term dependencies because they rely on a fixed number of layers and weights, which can make it difficult to model complex relationships across many time steps.
• Recurrent neural networks (RNNs) are designed to address the problem of long-term dependency by using a hidden state that can retain information about past inputs and incorporate that information into future predictions.
• However, even RNNs can struggle to capture very long-term dependencies, particularly if the input and output sequences are very long or if the relationships between variables are very complex.
• Other techniques, such as attention mechanisms and transformers, have been developed to better handle long-term dependencies in certain types of models, particularly those used for natural language processing and image recognition.