The probabilistic-seq2seq repository provides an advanced implementation of a Seq2Seq (Sequence to Sequence) model designed for probabilistic forecasting. While the standard Seq2Seq model is widely used for sequence prediction tasks, this version extends it to predict not only point estimates but also confidence intervals. This is particularly valuable for applications in time series analysis, where predicting the range of possible future outcomes is as important as providing an exact prediction. Use cases include:
- Weather forecasting
- Realized volatility prediction
- Demand forecasting
The model consists of several components:
- Encoder: Processes the input sequence and encodes it into a fixed-size context vector.
- Decoder: Takes the context vector and generates the output sequence.
- Attention Mechanism: (Optional) Focuses on relevant parts of the input sequence during decoding.
- Clone the repository:
git clone https://github.com/your-username/probabilistic-seq2seq.git
cd probabilistic-seq2seq