An article in the field of data science systematically explains, through visualization, why position encoding is necessary in the Transformer architecture for processing time series data. The article points out that the standard Transformer relies on the self-attention mechanism and cannot directly perceive the order information of elements in the input sequence, while the accuracy of time series data highly depends on the sequence of time steps. Therefore, it is necessary to inject temporal information into the model through specific position encoding schemes (such as absolute or relative position encoding) so that the model can correctly understand the temporal dependencies of the data and effectively capture key features such as trends and cycles.
When processing time-series data, the Transformer model loses position information due to the inability of the self-attention mechanism to perceive the order of inputs. To restore the sequence order, positional encoding needs to be introduced. This article, published by Towards Data Science, explains the evolution from scalar observations to the self-attention mechanism through visualization and highlights the crucial role of positional encoding in this process.