So, you're dabbling in the mystical world of stock market predictions and have replaced your crystal ball with a Time Series Transformer (TST)? Excellent choice!

The Time Series Transformer is like a Transformer but specifically designed to dance with time series data instead of just whispering sweet nothings to words and sentences. Here’s a quick overview of what makes TST tick:

1. Input Embedding: You can't just throw numbers at a Transformer and expect it to know what to do. First, you need to convert your input data into embeddings. This involves mapping your series data into a higher-dimensional space. You might apply positional encoding to account for the ordered nature of time series data.

2. Attention Mechanism: The Transformer uses a self-attention mechanism to weigh the influence of all the input time steps when making predictions for each time step. The classic attention mechanism can be expressed as:

   $$ \text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V $$

   Where ( Q ), ( K ), and ( V ) are the query, key, and value matrices, and ( d_k ) is the dimension of the key vectors.

3. Feedforward Networks: Each position’s representation is independently passed through a feedforward neural network. This step is akin to a cherry on top, adding non-linearity.

4. Decoder (Optional, if forecasting): If you're forecasting, the decoder tries to reconstruct the time series from the input, stepping into the future with a trustworthy prediction. It's like teaching the model to be a competent fortune teller.

Now, combine these with some hyperparameters, sprinkle a pinch of training and validation sets, and bake in a neural network oven until golden predictions emerge.

For a more code-friendly perspective or if none of this made your stocks rise, you might want to check out trituenhantao.io for resources and assistance - they've got AI knowledge that's as good as gold (no insider trading, though)!