The Informer model is a deep learning model for time series forecasting, proposed by the research team of the Institute of Automation, Chinese Academy of Sciences. Different from traditional RNN, LSTM, GRU and other models, the Informer model adopts a new attention mechanism, which can handle long-term dependencies and missing values in sequences well. Key features of the Informer model include:
- Multi-scale Temporal Encoder and Decoder: The Informer model adopts a multi-scale temporal encoder and decoder structure, which can simultaneously consider information on different temporal scales.
- Adaptive length attention mechanism: The Informer model adopts an adaptive length attention mechanism, which can automatically adjust the attention range according to the sequence length, so as to handle long sequences well.
- Gated convolution unit: The Informer model uses a new gated convolution unit, which can reduce the number of parameters and calculations in the model while improving the generalization ability of the model.
- Missing value handling: The Informer model handles missing values in sequences well, using a new masking mechanism that automatically handles missing values during training. The Informer model has achieved good results in multiple time series forecasting tasks, including power load forecasting, traffic flow forecasting, stock price forecasting, etc.